APO Productivity Databook 2017 - Asian Productivity Organization

2017 Asian Productivity Organization


Contents

1 Introduction 1.1 Databook 2017 1.2 List of Contributors 1.3 Map of Countries Covered

2 Overview 2.1 Global and Regional Economic Trends 2.2 Summary Findings

3 Economic Growth 3.1 Economic Scale and Growth 3.2 Catching Up in Per Capita GDP 3.3 Sources of Per Capita GDP Gap

4 Expenditure 4.1 Final Demand Compositions 4.2 Consumption and Investment 4.3 Expenditure-Side Growth Decomposition

5 Productivity 5.1 Per-Worker Labor Productivity 5.2 Per-Hour Labor Productivity 5.3 Total Factor Productivity 5.4 Sources of Labor Productivity Growth 5.5 Energy Productivity

6 Industry Perspective 6.1 Output and Employment 6.2 Industry Growth 6.3 Labor Productivity by Industry

7 Real Income 7.1 Real Income and Terms of Trade 7.2 Trading Gain and Productivity Growth

8 Development Policy 8.1 National Development Strategies

Appendix A.1 A.2 A.3 A.4 A.5 A.6 A.7

GDP Harmonization Capital Stock Rate of Return and Capital Services Hours Worked and Labor Compensation Other Data Industry Classification Data Publication and Visualization

References

viii ix 1 1 3 5 7 7 9 15 16 24 34 37 37 42 49 57 57 62 67 80 90 95 95 103 114 119 119 128 131 131 149 149 154 156 158 161 162 164 165

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Abbreviation Foreword

Contents

Box Box

1

Box

2

Box

3

Box

4

Box

5

Box 106 Box 107 Box 108 Box 109 Box 110 Box 111

PPP in the 2011 ICP Round System of National Accounts in Asia Size of the Informal Sector Turning Point in China Revising Myanmar’s Growths Sensitivity of TFP Estimates Labor Quality Changes Productivity of City Premature Deindustrialization Per-Worker Wage and Income Level Population and Demographic Dividend

20 32 50 53 56 88 89 193 110 130 145

GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015 GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2015 GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015 Population, 1970, 1980, 1990, 2000, 2010, and 2015 Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015 Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2015 Country Groups Based on the Initial Economic Level and the Pace of Catching Up Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2015 Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015 Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015 Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015 Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015 Output Growth and Contributions of Labor, Capital, and TFP, 1970–2015 Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2015 Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2014 Country Groups Based on the Current Economic Level and the Pace of Catching Up Output Growth by Industry, 2000–2015 Labor Productivity Growth by Industry, 2000–2015 Real Income and Terms of Trade, 1995–2000, 2000–2005, 2005–2010, and 2010–2015 National Development Strategies Asset Classification and Parameters in Hyperbolic Function Input-Output Tables and Supply and Use Tables Average Ex-Post Real Rate of Return in Asia Sources of Labor Data Industry Classification – Concordance with ISIC Rev.3 Industry Classification – Concordance with ISIC Rev.4

17 18 22 26 27 29 33 37 59 60 63 65 74 86 91 95 107 114 120 133 154 154 157 159 162 163

Table Table 111 Table 112 Table 113 Table 114 Table 115 Table 116 Table 117 Table 118 Table 119 Table 110 Table 111 Table 112 Table 113 Table 114 Table 115 Table 116 Table 117 Table 118 Table 119 Table 120 Table 121 Table 122 Table 123 Table 124 Table 125


Table 126

iv

Figure 112 Figure 113 Figure 114 Figure 115 Figure 116 Figure 117 Figure 118 Figure 119 Figure 110 Figure 111 Figure 112 Figure 113 Figure 114 Figure 115 Figure 116 Figure 117 Figure 118 Figure 119 Figure 120 Figure 121 Figure 122 Figure 123 Figure 124 Figure 125 Figure 126 Figure 127 Figure 128 Figure 129 Figure 130 Figure 131 Figure 132 Figure 133 Figure 134 Figure 135 Figure 136 Figure 137 Figure 138 Figure 139 Figure 140 Figure 141 Figure 142 Figure 143 Figure 144 Figure 145 Figure 146 Figure 147 Figure 148 Figure 149 Figure 150 Figure 151 Figure 152

GDP Growth of Asia, the EU, Japan, and the US, 1970–2015 Share of Asia in World GDP in 2015 and Projection for 2022 Price Level Indices of GDP, 2011 Regional GDP of Asia and the EU, Relative to the US, 1970–2015 GDP of China, India, and Japan, Relative to the US, 1970–2015 Regional GDP of South Asia, ASEAN, CLMV, and GCC, Relative to the US, 1970–2015 Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, 2000–2010, and 2010–2015 Correlation of GDP Growth, 1990–2000 Correlation of GDP Growth, 2000–2015 Share of Asian Population in the World, 2015 Per Capita GDP using Exchange Rate of Japan and Australia, Relative to the US, 1970–2015 Per Capita GDP using Exchange Rate of the Asian Tigers, Relative to the US, 1970–2015 Per Capita GDP of Japan, the EU, and Australia, Relative to the US, 1970–2015 Per Capita GDP of the Asian Tigers, Relative to the US, 1970–2015 Per Capita GDP of China, India, and ASEAN, Relative to the US, 1970–2015 Per Capita Non-Mining GDP in Oil-Rich Countries and Japan, 2015 Initial Level and Growth of Per Capita GDP, 1970–2015 Labor Productivity and Employment Rate Gap Relative to the US, 1990 and 2015 Sources of Per Capita GDP Growth, 1990–2000 and 2000–2015 Share of Female Employment Employment Rates, 1970, 1990, and 2015 Final Demand Shares in GDP of China, 1952–2015 Final Demand Shares in GDP of the US, 1929–2015 Final Demand Shares in GDP, 1995 and 2015 Ratio of Dependent Population and Consumption Share in GDP, 2015 Export and Import Shares in GDP, 1995 and 2015 Long-Term Trend of Household Consumption Share in GDP, 1970–2015 Household Consumption by Purpose, 2015 Engel Curve of Japan during 1949–2015 and Levels of Asian Countries in 2015 Long-Term Trend of Investment Share in GDP, 1970–2015 FDI Inflows, 2000–2015 FDI Inflow Ratio and Business Environment, 2000–2015 Investment Share by Type of Asset, 1970 and 2015 Long-Term Trend of Net Export Share in GDP, 1970–2015 Final Demand Contributions to Economic Growth, 1990–2010 and 2010–2015 Impacts of Global Financial Crisis and Recoveries, 2007–2012 Impacts of Asian Financial Crisis, 1997–1998 Final Demand Decomposition of Real GDP Growth, 1970–2015 Labor Productivity Level by Per-Worker GDP, 2015 Labor Productivity Trends of China and India, 1970–2015 Labor Productivity Level Relative to the US, 1970–2015 Labor Productivity Gap by Per-Worker and Per-Hour GDP Relative to the US, 2015 Labor Productivity Trends in Japan and the Asian Tigers, 1970–2015 Labor Productivity Growth, 1970–2015, 1970–1990, and 1990–2015 Labor Input Growth, 1970–2015, 1970–1990, and 1990–2015 Labor Productivity Trends of Japan in 1885–2015 and Levels of Asian Countries in 2015 Time Durations Taken to Improve Labor Productivity by Japan and the Asian Tigers TFP Growth, 1970–2015, 1970–1990, and 1990–2015 Sources of Economic Growth, 1970–2015 Contribution Shares of Economic Growth, 1970–2015 Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2015 Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2015

15 15 16 19 19 21 23 24 25 25 28 28 30 30 30 31 31 34 35 36 36 38 39 39 40 41 42 43 44 45 45 47 47 48 49 52 52 54 58 58 61 62 63 64 64 66 66 68 69 69 70 70

v


Figure Figure 111

Contents

Figure 153 Figure 154 Figure 155 Figure 156 Figure 157 Figure 158 Figure 159 Figure 160 Figure 161 Figure 162 Figure 163 Figure 164 Figure 165 Figure 166 Figure 167 Figure 168 Figure 169 Figure 170 Figure 171 Figure 172 Figure 173 Figure 174 Figure 175 Figure 176 Figure 177 Figure 178 Figure 179 Figure 180 Figure 181 Figure 182 Figure 183 Figure 184 Figure 185 Figure 186 Figure 187 Figure 188 Figure 189 Figure 190 Figure 191 Figure 192 Figure 193 Figure 194 Figure 195 Figure 196 Figure 197 Figure 198


Figure 199 Figure 100 Figure 101 Figure 102 Figure 103 Figure 104 Figure 105

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Comparison of Sources of Economic Growth with OECD Countries, 2000–2015 Comparison of TFP Contribution Shares with OECD Countries, 2000–2015 Individual Countries’ Growth Accounting Decomposition, 1970–2015 IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2015 IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India, 1970–2015 Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2015 Capital Deepening, 1970–2015, 1970–1990, and 1990–2015 Capital Productivity Growth, 1970–2015, 1970–1990, and 1990–2015 Capital Productivity Trends in Japan and the Asian Tigers, 1970–2015 Capital Productivity Trends in China and India, 1970–2015 Sources of Labor Productivity Growth, 1970–2015 Contribution Shares of Labor Productivity Growth, 1970–2015 Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015 Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015 Decomposition of Labor Productivity Growth, 1970–2015 Shares of Asia in World Energy Consumption and CO2 Emission, 2014 Labor Productivity and Energy Productivity, 2014 Sources of CO2 Emission Growth, 2000–2014 Industry Shares of Value Added, 2015 Manufacturing Share and TFP Growth, 2000–2015 Industry Shares of Value Added in Manufacturing, 2015 Industry Shares of Value Added and Employment by Country Group, 1980, 1990, 2000, and 2015 Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2015 Industry Shares of Employment, 2015 Employment Share of Agriculture in Japan during 1885–2015 and Levels of Asian Countries in 2015 Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2015 Job Creation in Manufacturing, 1970–2015 Industry Origins of Economic Growth, 1990–2000 and 2000–2015 Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2015 Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2015 Industry Origins of Output Growth in Manufacturing, 1990–2000 and 2000–2015 Industry Origins of Regional Economic Growth, 1990–2000 and 2000–2015 Industry Origins of Asian Economic Growth, 2000–2015 Industry Origins of Economic Growth, 1970–2015 Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2015 Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–2015 Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–2015 Effect of Net Income Transfer on GDP, 1970–2015 Price of Crude Oil, 1986 January–2017 May Trading Gain Effect, 1990–2000 and 2010–2015 Real Income and Real GDP Growth, 1970–2015 Decomposition of Real Income Growth, 1970–2015 and 2000–2015 Decomposition of Real Income Growth, 1973–1979 and 1996–1998 Sources of Real Income Growth, 1970–2015 Trading Gain Effect and Labor Productivity Growth, 1970–2015 Trading Gain Effect and Value-added Share in Mining Sector, 1970–2015 Adjustment of FISIM FISIM Share in GDP, 2000–2015 Software Investment Ratio and GFCF Ratio to GDP, 2005 Adjustment of R&D R&D Share in GDP, 2015 Capital-Output Ratio, 1980 and 2015 Ex-Post Real Rate of Return in Asia, 1970–2015

72 73 76 78 78 79 81 81 82 82 82 82 83 83 84 90 92 92 96 97 97 98 99 100 100 101 102 104 105 106 108 109 111 112 115 116 117 121 122 122 123 124 125 126 128 129 150 150 151 152 153 155 156

Figure 107 Figure 108 Figure B1 Figure B2 Figure B3 Figure B4 Figure B5 Figure B6.1 Figure B6.2 Figure B7.1 Figure B7.2 Figure B8 Figure B9 Figure B10 Figure B11.1 Figure B11.2 Figure B11.3 Figure B11.4 Figure B11.5

Average Annual Hours Worked Per Worker Relative to the US, 2010–2015 Availability of COE Estimates Visualization in Asian Economy and Productivity Map

160 160 164

Revisions of PPP for GDP by the 2011 ICP Round Implementation of the 1968, the 1993, and the 2008 SNA Employee Share and GDP Level, 2015 Price of Labor Relative to Capital in China, Japan, and the Asian Tigers, 1970–2015 Official and Revised Estimates of Real GDP Growths in Myanmar Labor Income Share for Employees, 2015 Sensitivity of TFP Estimates by the Change of Income Share, 1970–2015 Contributions of Labor Quality to Growths in Japan and the US, 1955–2012 Average Schooling Years of Workers, 1970–2015 Per-Worker Labor Productivity Levels of Cities in PDB-City, 2015 Country Peaks of GDP Shares of Manufacturing Average Wage and Per Capita GNI, 2015 Distribution of the World’s Population in Different Regions, 1950–2100 Asian Countries’ Population Size and Projection, 1970, 2015, and 2050 Proportion of the Dependent Population, 2015 Demographic Dividend by Country, 1950–2100 Demographic Dividend by Country Group, 1950–2100

20 32 50 53 56 88 88 89 90 93 110 130 145 146 146 147 147

vii


Figure 106

Abbreviation ADB APO APO20

AEPM ASEAN ASEAN6 Asia24 Asia30 CLMV CPI COE ESRI EU EU15

EU28


FDI FISIM GCC GDP GFCF GNI ICP ILO IMF ISIC IT KEO Lao PDR LDCs NPISHs OECD PPP QALI QNA RCEP ROC R&D SNA TFP TPP UAE UN UNSD US

viii

Asian Development Bank Asian Productivity Organization 20 member economies of the Asian Productivity Organization: Bangladesh, Cambodia, the Republic of China, Fiji, Hong Kong, India, Indonesia, Islamic Republic of Iran, Japan, the Republic of Korea, the Lao PDR, Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam Asian economy and productivity map (see Appendix 7) Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, the Lao PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam Brunei, Indonesia, Malaysia, the Philippines, Singapore, and Thailand APO20 plus the People’s Republic of China, the Kingdom of Bhutan, Brunei, and Myanmar Asia24 plus GCC countries Cambodia, the Lao PDR, Myanmar, and Vietnam consumer price index compensation of employees Economic and Social Research Institute, Cabinet Office of Japan European Union 15 member economies of the European Union prior to enlargement: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom European Union: EU15 plus Bulgaria, Republic of Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovak Republic, and Slovenia foreign direct investment financial intermediation services indirectly measured Gulf Cooperation Council: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE gross domestic product gross fixed capital formation gross national income International Comparisons Program International Labour Organization International Monetary Fund International Standard Industry Classification of All Economic Activities information technology Keio Economic Observatory, Keio University Lao People’s Democratic Republic less developed countries non-profit institutions serving households Organisation for Economic Co-operation and Development purchasing power parity quality adjusted labor inputs quarterly national accounts Regional Comprehensive Economic Partnership Republic of China research and development System of National Accounts total factor productivity Trans-Pacific Partnership United Arab Emirates United Nations United Nations Statistics Division United States

Foreword A major challenge to sustaining growth continues to be raising productivity. The Asian Productivity Organization (APO), as the sole organization devoted to productivity in the Asia-Pacific, has endeavored to offer innovative solutions and assistance to its member economies not only for enhancing productivity but also for effectively dealing with the uncertain global business environment driven by fast-changing, emerging technologies that are drastically altering our lives and the business environment. Measuring productivity is an important part of the APO project portfolio, as it is tasked with monitoring productivity gaps for member economies. At the same time, monitoring social, technological, economic, environmental, and political changes by governments to foresee trends is equally pivotal, so that they can align the most needed policies with national development blueprints, as well as create a favorable environment for industries to adapt and prepare quickly for new opportunities and challenges. I am pleased to invite readers to utilize this new edition of the APO Productivity Databook. It presents an analytical report on recent and long-term productivity and economic performance in the Asia-Pacific and reference economies. My gratitude goes to the chief expert of this project, Professor Koji Nomura of Keio University, for his contributions to developing methods for the comprehensive analyses of productivity. I hope that readers will find this a useful reference on the productivity status of countries in the APO region.

Santhi Kanoktanaporn

ix


Secretary-General Asian Productivity Organization Tokyo, September 2017

1.1 Databook 2012

1 Introduction 1.1 Databook 2017

1

This is the tenth edition in the APO Productivity Databook series. The Databook aims to provide a useful reference for the quality of economic growth in Asia. It presents authoritative estimates of productivity and its decomposition, which are comparable across countries at different development stages in the middle and long run. Productivity gains, which enable an economy to produce more for the same amount of inputs or to consume less to produce the same amount of outputs, are the only route to sustainable economic growth in the long run. Thus it follows that monitoring and improving national productivity capability are important targets of public policy. In this edition of the Databook, baseline indicators on economic growth and productivity are calculated for 30 Asian economies, representing the 20 Asian Productivity Organization member economies (APO20) and the 10 non-member economies in Asia. The APO20 consists of Bangladesh, Cambodia, the Republic of China (ROC), Fiji, Hong Kong, India, Indonesia, the Islamic Republic of Iran (Iran), Japan, the Republic of Korea (Korea), the Lao People’s Democratic Republic (Lao PDR), Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam. The 10 nonmember economies in Asia are: the People’s Republic of China (China), the Kingdom of Bhutan (Bhutan), Brunei, Myanmar, and the Gulf Cooperation Council (GCC) that consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UAE). In addition, Australia, the European Union (EU), Turkey, and the United States (US) are included as reference economies. This edition covers the period from 1970 to 2015.

Based on the growth accounting framework in PDB, the sources of economic growth in each economy are further decomposed to factor inputs of labor and capital and total factor productivity (TFP) for 21 Asian economies – Bangladesh, Cambodia, the ROC, Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, the Lao PDR, Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, Vietnam, and China – along with the US as a reference economy. It is a notable achievement that the estimates on TFP for the Lao PDR are newly developed in this edition of the Databook. In addition, the main aggregates for the Lao PDR are backwardly estimated from 1970 (the starting year was 1981 in Databook 2016). This edition also attempts to revise the official estimates of the economic growth in Myanmar, which might have been significantly overstated since the latter half of the 1990s, as indicated by The Economist Intelligence Unit (2010) and ADB (2017). The revision process is described in Box 5. To analyze the overall productivity performance as well as productivity subsets (e.g., labor productivity and capital productivity), the Databook constructs the estimates of capital services, which provides

1


The productivity measures in the Databook are based on the official data and our own estimates collated for the APO Productivity Database (PDB). This is a joint research effort between the APO and the Keio Economic Observatory (KEO), at Keio University, Tokyo, since September 2007. In Asian countries, recent significant revisions based on the System of National Accounts 2008 (2008 SNA), which is the latest version of the international statistical standard for the national accounts by the United Nations (2009), have resulted in updates for Sri Lanka as of March 2016 and Japan and Turkey as of December 2016. While there are movements to upgrade the national accounts, some countries such as Cambodia, the Lao PDR, and Nepal, have yet to fully introduce the earlier version 1993 SNA. Because the varying SNA adaptions among the economies can result in discrepancies between data definitions and coverage, data harmonization is necessary for comparative productivity analyses. The Databook attempts to reconcile these national accounts variations which are based on the different concepts and definitions. This is done by following the 2008 SNA and providing harmonized estimates for better international comparison. The GDP harmonization process including capitalization of software and research and development (R&D) is provided in Appendix 1.

1 Introduction

an appropriate concept of capital as a factor of production, as recommended in the 2008 SNA. The fundamental assumption in measuring capital services is proportionality between the (productive) capital stock and capital services in each type of asset. Thus, the growth rates of capital services can differ from that of capital stock only at the aggregate level. The assumption and data in measuring capital stock is presented in Appendix 2. For aggregating different types of capital, the user cost of capital by type of asset is required. The outline of the methodology to measure price and volume of capital service is presented in Appendix 3. The labor share is one of the key factors to determine the TFP growth. However, the estimates on the compensation of employees (COE) are not fully available in the official national accounts in Asian countries (i.e., Bangladesh, the Lao PDR, Pakistan, and Vietnam). At KEO, the comprehensive database (PDB-L) on number of workers, hours worked per worker, and hourly wages, which are cross-classified by gender, education attainment, age, and employment status, has been developed for the past few years. The COE are estimated based on this work-in-progress database and used for the countries in which the official estimates are not available. In addition, the compensation of self-employed and contributing family workers, which tend to have a larger share in total employment in less developed countries, have to be estimated to determine the total labor cost. In this edition of the Databook, the harmonized assumption on the hourly-wage differentials between employees and self-employed and contributing family workers in the most detailed category of labor in PDB-L is newly applied. The methodology to measure labor input is presented in Appendix 4.


The structure of the Databook is as followed. The recent trends in global and regional economic growth and the summary findings are presented in Chapter 2. In order to understand the dynamics of the long-term economic growth within Asia, Chapter 3 details countries’ diverse development efforts and achievements, through cross-country level comparisons of GDP. Decompositions of GDP, which is defined by three approaches in SNA: production by industry, expenditure on final demand, and income to factor inputs, are valuable in understanding the structure, and in turn the behavior, of an economy. Chapter 4 presents the demand side decomposition analyzing the sources of countries’ expenditure growths. The estimates of final demands are newly added for the Lao PDR and Myanmar in this edition of the Databook. In Chapter 5, the supply side decompositions of economic growth and labor productivity improvement are analyzed in each country and region. In this edition, the country aggregations of capital and labor inputs are newly based on the estimates of PPP for capital and labor inputs, respectively. This chapter also provides the energy productivity performance to reflect the impending need to improve energy efficiency as a policy target for pursuing sustainable growth. The preliminary digest of our work-in-progress database on productivity of a city (PDB-City) is presented in Box 8. The different composition of economic activities among countries is one of the main sources of the huge gap in average labor productivity at the aggregate level. The industry structure is presented in Chapter 6. Chapter 7 focuses on real income to evaluate an improvement in the terms of trade. Finally, Chapter 8 was newly added in this edition of the Databook to present the summary of the national development strategies in the APO member economies. The official national accounts and metadata information used for constructing the APO Productivity Database 2017 has been collected by the national experts in APO member economies and research members at KEO. The names of these contributors are listed in Section 1.2. The submitted data was then examined and compiled at KEO, where further information was collected on labor, production, prices, trades, and taxes as required. This edition effectively reflects the revisions to the official national accounts and other statistical data published through May 2017. The project was managed by Koji Nomura (Keio University), under the consultancy of Professor Dale W. Jorgenson (Harvard University) and Professor W. Erwin Diewert (University of British Columbia), and with coordination by Yasuko Asano (APO). The text, tables, and figures of this edition were authored by Koji Nomura and Fukunari

2

1.2 List of Contributors

Kimura (Keio University), with support from the research assistants Shinyoung Oh, Naoyuki Akashi, Hiroshi Shirane, Shiori Nakayama, Daisuke Matsuoka, Kei Iwai, and Yurika Katayama. The Databook project appreciates Eunice Ya Ming Lau for her contribution to developing the foundation of the Databook series during her stay at KEO and Trina Ott for her review of the draft. In particular, we express our heartfelt condolences on the death of our colleague, Ms. Navilini Singh. She served as the national expert for Fiji for the APO Productivity Databook since 2011. May she rest in peace.

1

1.2 List of Contributors

Dr. Koji Nomura APO Productivity Database Project Manager, Professor, KEO, Keio University, 2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japan

Dr. Fukunari Kimura Professor, Department of Economics, Keio University

Research Members at KEO Ms. Shinyoung Oh Mr. Naoyuki Akashi Mr. Hiroshi Shirane Ms. Shiori Nakayama Mr. Daisuke Matsuoka Mr. Kei Iwai Ms. Yurika Katayama APO Officer Ms. Yasuko Asano Program Officer, Research and Planning Department, Asian Productivity Organization, 1-24-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

National Experts Bangladesh Mr. Ziauddin Ahmed Joint Director, Bangladesh Bureau of Statistics, Ministry of Planning, Parishankhyan Bhaban, E-27/A, Agargaon, Sher-e-Bangla Nagar, Dhaka-1207

Cambodia Mr. Chettra Keo Director, National Accounts Department, National Institute of Statistics, #386 Preah Monivong Blvd, Phnom Penh

Republic of China Ms. Ming-Chun Yu Chief, National Accounts Section, Bureau of Statistics, Directorate-General of Budget, Accounting, and Statistics (DGBAS), Executive Yuan, No. 2, Guangzhou St., Zhongzheng District Taipei, 10065

Fiji Ms. Navilini Singh Senior Statistician, Economics Statistics, Fiji Bureau of Statistics, Rata Sukuna House, PO box 2221, Government Building, Suva

India Dr. Kolathupadavil Philipose Sunny Group Head (Economic Services), National Productivity Council, Lodhi Road, New Delhi, 110003

Indonesia Ms. Ema Tusianti Head of Cross Sector Statistical Analysis Section, Statistics Indonesia, Jl. Dr. Sutomo No.6-8, Jakarta

Islamic Republic of Iran Mr. Behzad Mahmoodi Head of Goods and Services Analyzing Section (GSA), Central Bank of IR Iran, Economic Statistics Department, Ferdousi Ave., Tehran

3


Authors of This Report

1 Introduction

Japan Mr. Yutaka Suga Research Official, National Wealth Division, National Accounts Department, Economic and Social Research Institute, Cabinet Office, Government of Japan, 3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo, 100-8970

Lao PDR Ms. Salika Chanthalavong Head, National Account Division, Economic Statistics Department, Lao Statistics Bureau, Ministry of Planning and Investment, Vientiane

Malaysia Ms. Hezlin Suzliana Binti Abdul Halim Assistant Director, Department of Statistics, Malaysia, National Accounts Statistics Division, Ting.3, Unit 01-05, Wisma Minlon, Batu 12 Lebuhraya Sg. Besi, 43300 Seri Kembangan, Selangor

Mongolia Ms. Bayarmaa Baatarsuren Director of National Accounts Division, Economic Statistical Department, National Statistics Office of Mongolia, Government Building III, Ulaanbaatar-20a

Nepal Mr. Rajesh Dhital


Director, Central Bureau of Statistics, Ramshahpath, Thapathali, Kathmandu

4

Pakistan Mr. Fazil Mahmood Baig Director, National Accounts Wing, Statistics Division, Pakistan Bureau of Statistics, 21 Mauve Area, Statistics House, G-9/1, Islamabad

Philippines Ms. Ma. Julieta P. Soliven Statistician E, Philippine Statistics Authority, 16th Floor Eton Cyberpod Centris Three, EDSA cor Quezon Ave., Quezon City

Sri Lanka Mr. Weerasinghe Wasala Mudiyanselage Ananda Sarath Premakumara Additional Director General (Statistics I), Department of Census and Statistics, 5th Floor, Rotunda Tower, No. 109, Galle Road, Colombo 03

Thailand Mr. Wirot Nararak Director, National Accounts Office, National Economic and Social Development Board, 962 Krung Kasem Road, Pomprab, Bangkok 10100

Vietnam Mr. Duong Manh Hung Deputy Director, National Accounts Department, General Statistic Office of Vietnam, No. 6 Hoang Dieu, Ba Dinh District, Hanoi

5


APO member economies Non-member Asian economies included in Databook 2017

1.3 Map of Countries Covered

1.3 Map of Countries Covered

1

2.1 Global and Regional Economic Trends

2 Overview 2.1 Global and Regional Economic Trends

2

The year 2016 would be remembered as the year when anti-globalism rose in advanced economies, marked by the vote for “Brexit” in the UK, and the election of US President Donald Trump. However, the overall economic situation was fair, with a continued recovery from a long recession in developed economies, and steady economic growth in most of the Asian developing economies. In Asia30 and East Asia, the average annual growth of GDP at constant market prices in 2010–2015 was 5.3% and 5.6%, respectively (Table 3 in Section 3.1). The growth slowdown in China and the decline of world trade seemed to stabilize. Latecomers in ASEAN, India, and other Asian developing countries sustained rapid growth. Prolonged low food and fuel prices helped most of the Asian economies keep inflation low and sustain the pace of economic growth. Advanced economies maintained a path of slow recovery. Among them, the US economy performed better than others. The average annual growth of GDP at constant market prices in 2010–2015 in the US was 2.1%. The unemployment rate dropped to 4.7% in December 2016, which was low by US standards, and continued to decline. The European economy also presented some sign of recovery. There, the average annual growth rate of GDP at constant market prices in 2010–2015 in the EU15 and the EU28 were 0.9% and 1.0%, respectively. The Japanese economy was also on the course of recovery though its potential growth rate was low. The annual growth of GDP at constant market prices in the same period in Japan was 1.0%. The unemployment neared 3%. The recent World Economic Outlook by the IMF (2017) shows growth forecasts for the year of 2017 better than the previous year, particularly for the US and Japan. Although the growth slowdown in China continued for three years, it seems to have stabilized as a “new normal,” achieving 7.6% in the average annual growth of GDP at constant market prices in 2010– 2015. There, drastic reform in the domestic economy continues. Along with the Chinese economy, Korea also slowed, having 3.0% average annual growth in the same period.

The South Asian countries have not taken full advantage of international production networks, though some have been successful in connecting with slow global value chains in labor-intensive industries. The per capita GDP using exchange rate in 2015 in Nepal, Bangladesh, Pakistan, and India was $790, $1,230, $1,390, and $1,610, respectively. The growth perspectives of the Asian economies are fair, though the slowdown of China seems to continue. However, there are both internal and external factors with which steady economic growth could be jeopardized. The prime concern is on protectionism in advanced economies. Results in the

7


Latecomers in ASEAN, Cambodia, the Lao PDR, and Myanmar, have steadily grown in the past two decades, reaching $1,210, $1,870, and $930 in the per capita GDP using exchange rate in 2015, respectively (Table 5 in Section 3.1). However, the easy catch-up period is almost over. To achieve sustained economic growth, they have to engage in international production networks. “Thai plus one” investment by machinery parts producers that set up fragmented satellite factories off Thailand appear to have slowed recently. Vietnam achieved deeper involvement in international production networks and had $2,130 per capita GDP. However, the ratio of manufacturing value added to GDP is still 15% in 2015 (Figure 71 in Section 6.1 and Figure B.9 in Box 9). Growth of supporting industry and industrial agglomeration is a hopeful anticipation. The Philippines and Indonesia are also in the process of forming an efficient industrial agglomeration with $2,900 and $3,400 in per capita GDP. Thailand, Malaysia, and Singapore reached $6,000, $9,560, and $53,600 in per capita GDP though they struggled with the industrial upgrading and the creation of innovation hubs.

2 Overview

UK EU membership referendum and the US presidential election point to a rise of anti-globalization sentiment. Brexit will certainly work as a headwind against deepening economic integration in Europe. Immediately after his inauguration, President Trump announced that the US would step out of the Trans-Pacific Partnership Agreement (TPP). The Trump administration may further extend protective measures in international trade. The world, in particular East Asia, is tightly connected by global value chains. Therefore any trade deterrent may disturb the functions of international production networks. The root of anti-globalization sentiment is complex. Some literature assumes that globalization or freer trade and investment worsens income distribution in advanced economies. The logical basis of the argument is the so-called Stolper-Samuelson (S-S) theorem in the Heckscher-Ohlin (HO) model; it claims that freer trade makes capitalists or skilled labor better off, while unskilled labor is worse off in advanced economies. However, the real world may not meet some of the basic settings and assumptions in the model. First, while the HO model assumes the perfect domestic mobility of capital/skilled labor and unskilled labor between industries, the reality seems to suffer from slow industrial adjustments and labor replacements. Rather than adjusting for income distribution between different factor holders, smoothing labor replacements across industries and firms as well as regions may be a more urgent policy agenda. Second, although the benchmark HO model has only two productive factors, the real world seems to be proxied by a model with three or more factors. If so, the effect of freer trade may be much more complicated. For example, demand for labor with the least human capital does not seem to decline; rather, some sort of the mid-class labor may face less demand. Third, the default HO model does not include productivity growth over time. It does not take into account changes in human capital either. These factors may also cause a departure from the S-S theorem. It is true that technology has become internationally mobile and the great convergence of income levels between advanced economies and newly developed economies has occurred since the 1980s as Baldwin (2016) claims. Therefore, advanced economies should accelerate industrial adjustments. However, it does not necessarily mean that income distribution issues become aggravated. Indeed, while the US and the UK have experienced increases in the income shares of the top 1% population since the 1980s, Japan and Germany have not.


Although the fear of protectionism remains, some moves to counter this have emerged. Because President Trump is embroiled in domestic politics, the formation of a trade team may be delayed. Additionally, the administration has significant room for discretion in security and trade issues, making it difficult to predict what will happen with US diplomacy. In time, US diplomacy may become more predictable as the White House staff involvement increases. A cascading win by Mr. Emmanuel Macron in the French presidential election mitigated the fear of expanding extreme right power in Europe. However, some noneconomic factors such as terrorism may change the political atmosphere at any time. Asian countries are not immune from terrorism and therefore the containment of terrorism is an essential element of the political agenda for all countries in the world. On a positive note, at the G20 Hamburg Summit in July 2017, the Japan-EU Economic Partnership Agreement (EPA) was announced. This advocates the intention of both sides to maintain the momentum of a free trade agenda, resisting the possible wind of protectionism. Because US exports will become relatively disadvantageous in the Japanese and EU markets, the policy demand for freer trade may be strengthened in US politics. East Asia can also join the freer trade initiative. Although the US stepped out of TPP, the remaining 11 negotiating countries began consideration of making TPP effective among 11 or 11 minus alpha countries. This is a meaningful attempt because the text of TPP has a high value as a model free trade agreement,

8

2.2 Summary Findings

even without the US, in terms of the level of liberalization and international rule making. The key is whether the countries can agree and validate TPP without changing much text, in addition to Article 30.5 which specifies the condition of validating TPP. Each country may have a compromise in the text of TPP. However, changing the text may lead to negotiations and complications. The question is whether some slowmoving countries can enter in the second round and validate TPP with 11 minus alpha countries quickly. Once TPP is in effect, an anticipated domino effect will attract new applicants, possibly even the US.

2

The Regional Comprehensive Economic Partnership (RCEP) also poses a significant challenge. Although ASEAN plus six countries started negotiating RCEP in 2013, progress was slow. However, there are signs of accelerating negotiations from early 2017. Because 2017 marks the 50th anniversary of the ASEAN, the need for ASEAN member states create some memorable achievements can aid in the progress of RCEP negotiations and potentially a good outcome for presenting ASEAN centrality. Thusly, the negotiation team of ASEAN began aggressive work for concluding RCEP. Of concern is a tradeoff between the speed of negotiation and the quality of agreement. At some point in time, RCEP should be designed as multi-layered with the less-complex segments concluded quickly. In the globalizing world, careful macroeconomic management is essential. The tapering of the US from long-lasting monetary easing will soon be realized if the US economy continues to strengthen. Although the current management of macroeconomic fundamentals is much better than that in the era of Asian currency crisis, the financial world is also much more globalized now. A slight shift may trigger sudden massive outflows of capital, resulting in a speculative attack. The financial authority must monitor asset and financial markets with scrutiny.

2.2 Summary Findings Asia’s economic vitality warrants considerable attention to the rapid and vigorous changes in its economic performance in the short run. To fully understand this economic dynamism, it is essential to grasp its growth performance, structural changes, and the advancement of its economic development within a context of its middle- and long-term performance. Asia, in particular, consists of a variety of countries at different development stages, with diversified resource endowments, and under various political regimes. The APO Productivity Databook is intended to be a useful reference for the quality of economic growth. It provides authoritative estimates of productivity and its decomposition, which are comparable across countries at different development stages in the middle and long run. International comparisons of economic performance are never a precise science; instead, they are fraught with measurement and data comparability issues. Despite best efforts in harmonizing data, some data uncertainty remains. Operating within a reality of data issues, some of the adjustments in the Databook are necessarily conjectural, while others are based on assumptions with scientific rigor. In addressing this shortcoming, findings drawn from the research are cross-referenced against other similar studies. Such magnitude of variations in the economic indicators is often subject to a certain degree of data uncertainty.

Recent economic growth of Asia u In terms of exchange-rate-based GDP, China overtook Japan in 2010 as the largest economy in Asia

and the second largest economy in the world, after the US. On this measure, the Asia30 was 36% and 48% larger than the US and the EU15 in 2015, respectively (Table 1).

9


Bearing in mind these caveats, the main findings from our analysis are as follows:

2 Overview

u Based on GDP adjusted for purchasing power parity (PPP),1 the weight of the world economy is

even more tilted toward Asia, with the Asia30 1.75 times and 1.98 times larger than the US and the EU15 in 2015, respectively. China has overtaken Japan as the largest Asian economy since 1999. In 2013 China overtook the US as the largest economy in the world, 10% larger relative to the US in 2015. India surpassed Japan, replacing it as the second largest economy in Asia in 2009. In 2015, the total GDP of the three largest Asian economies alone was 82% larger than the US economy (Table 2 and Figure 5). u During the period 1990–2015, the Asia30 grew at 5.4% on average per annum, compared with

2.4% and 1.6% in the US and the EU15, respectively. Japan was the slowest growing economy among the Asia30 at 1.0%, compared with 24 of the 30 Asian economies with over 4.0% of annual economic growth (Table 3 and Figure 1). u In the period 2010–2015, China and India have emerged as the driving forces propelling Asia for-

ward, accounting for 55% and 18% of regional growth, respectively (Figure 7). u The global financial crisis slowed Asia30’s growth significantly from a recent peak of 8.0% during

2006–2007, to 4.8% during 2007–2008 and further to 3.9% during 2008–2009, before rebounding strongly to 8.0% during 2009–2010. This is in comparison to the deep recession of –2.8% and –4.5% experienced by the US and the EU15, respectively, during 2008–2009 (Figure 1). u The correlation coefficients between China and other Asian economies strengthened between the

two decades. This suggests that China has become more integrated within the Asian economy. For most Asian countries, the correlation with the US and the EU15 has also grown stronger (Figures 8 and 9). Catching up in per capita GDP u Our results show the outcome of the dramatic development effort of the four Asian Tigers.2 Singa-

pore and Hong Kong have managed to close a per capita GDP gap with the US of around 60% in just under four decades. Singapore has even surpassed the US since 1993, and in 2015 its per capita GDP was 53% higher. In contrast, veteran Japan has fallen behind, widening its gap with the US to 28%. In 2015, the ROC’s and Korea’s per capita GDP was 83% and 65% of the US level, respectively (Table 6 and Figure 14). u Despite their rapid growth, due to their population, per capita GDP of China and India was 26%

and 11% of the US in 2015, respectively. However, this represents a tenfold increase in China’s relative per capita GDP over the last four decades. The level achieved by the Asia30 was 22% of the US, indicating that there is ample room for catch-up (Table 6 and Figure 15). u Asia’s huge per capita GDP gap with the US is predominantly explained by its labor productivity

gap. With the exception of the Asian Tigers, GCC, Japan, and Iran, all Asian countries have a labor productivity gap of 50% or higher (Figure 18).


u For most countries in Asia, the majority of per capita GDP growth can be explained by improve-

ment in labor productivity. However, the employment rate contribution relative to labor productivity was also highly significant in Singapore, Malaysia, Korea, and the ROC in 2010–2015 (Figure 19). 1: This Databook based on the new PPP estimates of the 2011 International Comparisons Program (ICP) round published in April 2014. This has the significant effect of raising the relative sizes of Asian economies against the base economy, the US. 2: Refers to Hong Kong, Korea, Singapore, and the ROC.

10


u There is a significant variation in Asia’s employment rate from 25% to over 60% at present. The

employment rate has been rising in most Asian countries and is more than 10 percentage points above the US in Singapore, Myanmar, Cambodia, Vietnam, and Thailand (Figure 21).

2

Changes in demand composition u With a few exceptions, household consumption is the biggest component of final demand. In

recent years, Asia30’s consumption ratio has dropped to 48.4% of GDP in 2015, largely reflecting the trend in China. This compares to 68.1% in the US, 56.4% in the EU15, and 57.7% in Australia (Table 8). u The share of household consumption in GDP tends to be more volatile, dropping in countries that

are undergoing rapid development. As countries get richer, the household consumption share tends to rise. At the other end of the spectrum, countries with low income and a high dependent population (under-15, over-65) sustain a high consumption ratio to GDP (Figures 24 and 25). u Overall, Asia invests more than the US/EU15 as a share of its GDP. Lately this gap has been widen-

ing. Historically, Australia’s investment share has been sandwiched between that of Asia and the US/EU15. In 2015, the Asia30 invested 35.4% of its GDP, compared with 20.3% for the US, 19.5% for the EU15, and 25.7% for Australia (Table 8 and Figure 30). u China faces huge internal and external imbalances. The investment share of GDP (at 45.7%), as the

biggest component in final demand and the household consumption share, plummeted to 37.0% in 2015. In contrast, the weight of net exports has been rising in the past decade, although it is declining in recent years due to weak foreign demand (Figure 22). u GCC economies are unusually skewed towards net exports because of their oil. Net exports ac-

counted for 18.7% of final demand in 2010, compared with Asia30’s 3.3% and China’s 3.6%. Only the US and South Asia run trade deficits of a more significant nature, which accounted for –3.4% and –4.9% of final demand, respectively, in 2010 (Table 8). u According to the cross-country version of Engel’s Law, basic necessities will account for a high pro-

portion of household consumption for a lower per capita income group and vice versa. Lowerincome countries spend 30–50% of total consumption for food, which corresponds to Japan’s experience in the 1950s and the 1960s (Figures 28 and 29). Labor productivity u For most Asian countries, the per capita GDP gap with the US is largely explained by labor produc-

tivity shortfalls of 80% or more against the US level. Only Singapore and Hong Kong have effectively closed that gap. The relative labor productivity of the Asia24 was 21% of the US in 2015 (Table 9 and Figure 39).

the low-income countries appeared to experience a labor productivity growth spurt in 2010–2015. Mongolia achieved the fastest labor productivity growth of 7.7% on average per year in this period, followed by China’s 7.2%, the Lao PDR’s 6.9%, Sri Lanka’s 5.7%, India’s 5.0%, and Cambodia’s 4.9%; this compares with 1.2–1.3% in the Asian Tigers, 0.7% in Japan and the US, and 0.6% in the EU15 (Table 10 and Figure 41).

11


u Growth of per-worker GDP in Asia has outstripped that in the US, allowing catch-up. In particular,

2 Overview

u The productivity gap based on GDP per hour is generally wider between Asian countries and the

US. While the adjustments are negligible for most Asian countries, the productivity gap significantly widened by 9–25 percentage points for the Asian Tigers, suggesting that people work much longer hours than in the US (Figure 42). u Most Asian countries experience faster growth in GDP per hour than the US. Among them, China’s

performance is the most outstanding, with average annual productivity growth doubling from 4.5% to 8.4% between 1970–1990 and 1990–2015, compared to the US at 1.5% and 1.7% over the same periods (Figure 44). u Mapped onto Japan’s historical trajectory of GDP per hour, most Asian countries cluster around the

level that Japan achieved in the 1950s and early 1970s, with the Asian Tigers being the clear frontrunners, sprinting away from the pack (Figure 46). Total factor productivity u Of the 21 Asian countries compared, 11 experienced faster TFP growth than the US over the period

1970–2015, with China in a league of its own. Its TFP growth was at 3.0% on average per year, compared with those of Thailand at 1.3% in second place and the US at 0.7%. With TFP growing at 0.4% on average per year, Singapore’s productivity performance has been weak relative to its economic counterparts (Figure 48). u Over the past four decades, economic growth in Asia has been predominantly explained by the

contribution of capital input, but the role of TFP growth should not be underestimated. Its contribution accounted for over 25% of economic growth in seven of the 21 Asian countries compared, with it being most prominent in India (35%), China (34%), Sri Lanka (33%), and Japan (31%) (Figure 50). u The composition of economic growth is shifting over time. In the past two decades, the contribu-

tion of capital input (especially of non-IT capital) has been getting progressively smaller in Asia, falling to a share of below 52% on average, while the contribution of TFP is getting progressively more significant, rising to a share of above 40% on average in 2000–2015 (Figures 52 and 58). u The evident rise in the contribution of information technology (IT) capital is noteworthy. By the

2000s, it had risen to above 4% in most Asian countries compared, while accounting for around one-third of economic growth in Japan and the US. The allocation shift towards IT capital started two decades earlier in the US than in any Asian country (Figures 52 and 56). u Over the past decades, it has been observable that economic growth has decelerated in the early

starters (Japan and the Asian Tigers). Their experience lends support to the likelihood of an eventual slowdown in China; the question is more likely “when,” than “if.” TFP growth slowed from its former peaks achieved in the late 1970s or late 1980s until recent years when countries experienced TFP resurgence (Figure 55).


Capital deepening and capital productivity u Capital deepening appears to be an accompanying process of rapid economic development. The

early starters (i.e., Japan and the Asian Tigers) underwent more rapid capital deepening in the initial period whereas the reverse is true for the currently emerging Asian economies. For example, the rise in capital–labor ratio decelerated from 10.1% on average per year to 6.7% in Korea between 1970–1990 and 1990–2015, whereas it doubled in China from 5.5% to 10.5% (Figure 59).

12


u Capital deepening tends to go hand in hand with deterioration in capital productivity. China’s per-

formance is particularly impressive as its acceleration in capital deepening over the past two decades did not compromise its capital productivity as much as the early starters in the early period (Figure 60).

2

u Over a long period – stretching four decades – a downward trend in labor productivity growth can

be seen among the early starters, but there is a step-up in China and India. Singapore’s productivity performance, albeit robust compared with other mature economies like the US, has been very modest against its Asian counterparts (Figure 67). Industry structure u Evidence supports the view that a country’s industry structure transforms with its economic devel-

opment. There is a broad negative correlation between the share of agriculture in total GDP and per capita GDP. Finance, real estate, and business activities increase in weight as countries move up income levels, whereas mining is the sector that defines the oil-exporting countries (Figure 71). u Manufacturing is a significant sector, accounting for over 20% of total value added in eight Asian

countries in 2015. It is particularly prominent in China, Thailand, and the Philippines, where over 1.5% of annual TFP growths are measured in 2000–2015 (Figure 72). Asian manufacturing is dominated by machinery and equipment in the richer Asian economies while their poorer counterparts concentrate on light manufacturing such as textiles and the food industry (Figure 73). u While Asian countries are diversifying away from agriculture, the sector still dominates employ-

ment, accounting for 33% of total employment in 2015 for the Asia30, down from 61% in 1980. Its share in total value added decreased more moderately, from 14% to 9% over the same period. Shifting out of agriculture into more efficient sectors will boost economy-wide productivity (Figures 74 and 77). u Manufacturing is a main absorption sector for workers who have been displaced from the agricul-

ture sector, especially in the initial stages of economic development. In Korea and the ROC, expansions to manufacturing output could account for the increase of employment in the 1970s and the 1980s. In the 1990s and 2000s, however, the manufacturing sector was no longer an absorption sector of employment, regardless of the sound expansion of production in this sector. Since 2010, the output growths in manufacturing deteriorated, but they had a positive impact on increasing jobs. (Figure 79). Industry origins of economic growth u Our results support the observation that China and India have taken different development paths,

u In contrast, growth in India has always been more driven by services, the contributions of which

are 61% in the 1990s and 64% in 2000–2015, while manufacturing usually contributes one-fifth or less (Figures 81 and 82).

13


with the former relying more on the traditional growth engine of manufacturing and the latter on services. In the past two and a half decades China has been undergoing a slight transition, with its growth shifting away from manufacturing-driven to more services-driven. In the period 2000– 2015, the contributions to economic growth by manufacturing and services were 34% and 46%, respectively, compared with 42% and 35% in the 1990s (Figures 81 and 82).

2 Overview

u A total of 29% of Asia30’s regional growth originated from the expansion of manufacturing in

2000–2015, 77% of which was accounted for by China. China’s manufacturing alone contributed 22% to regional growth (Figure 85). u The importance of manufacturing as a contributor to overall labor productivity growth has never

waned in Korea and the ROC. However, manufacturing has never been a major contributor in India in its recent development process or in Hong Kong and Sri Lanka in 2000–2015 (Table 18 and Figure 88). Real income and terms of trade u Real GDP could systematically underestimate (or overestimate) growth in real income if terms of

trade improve (or deteriorate). It is generally observed that the trading gain effect is more significant in the short term than in the long term. Our findings confirm this observation, with the exceptions in some oil-exporting countries such as Kuwait and Brunei, where trading gain has always been positive and significant (Table 19 and Figure 96). u Positive net primary income from abroad also bolsters a country’s real income. In Japan and the

Philippines, net primary income from abroad has been rising steadily, albeit at different magnitudes. In Japan, it rose from 0.8% of GDP in 1990 to 3.8% in 2015, compared with 1.5% in 1990 and 41.8% in 2015 in the Philippines. Singapore’s historical margin fluctuates within a large range when compared with other rich economies – from +2.0% in 1997 to –7.0% in 2004, but on the whole, it has been more negative than positive (Figure 90). u Our results show that for most countries studied, the difference between growth of real GDP and

real income (reflecting the combined effect of trading gain and net primary income from abroad) was within the margin of ±20% over the long period from 1970–2015; Kuwait and Brunei appear to be the outliers (Figure 93). u The eight countries that have been enjoying a trading gain over 0.5% per annum in the past four

decades are all resource-rich countries. Among them, only Indonesia, Myanmar, and Iran managed to achieve a positive growth in labor productivity. In contrast, export-oriented, high productivity Asian countries have been facing a deteriorating trading gain position as a price of their own success (Figure 97).


Asia is a diverse regional economy in which countries have embarked on their own journey of economic development at different times and different paces. As shown by our analysis, nearly all countries are making concerted efforts to move away from agriculture and accumulate capital in order to improve their growth potential and catch up with the West. Their efforts are yielding results beyond just impressive growth rates. The evidence gained from our research confirms that countries’ capital accumulation is accompanied by strong productivity improvements. Through the statistics and data presented in this report, one manages to catch a glimpse of the current unparalleled economic dynamics inherent in the region.

14

3.1 Economic Scale and Growth

3 Economic Growth In the past quarter of a century, the story of the world economy belonged to Asia, featuring its steady rise in economic prowess. Before the mid-1980s, the fortune of Asia closely followed that of Japan, but 1988 marked the start of their paths decoupling (Figure 1). Since the early 1990s, Asian growth has been outperforming the West consistently. With the exception of 1997–1999, when the economy was adversely affected by the Asian financial crisis (Figure 37 in Section 4.3, p. 52), the Asia30 has been growing faster than the US and the EU15 by 3 to 4 percentage points on average per year.3

3

%

10 8 6 4 2 0 −2 −4 −6 1970

Asia30

Japan

US

EU15

1975

1980

1985

1990

1995

2000

2005

2010

2015

Figure 1 GDP Growth of Asia, the EU, Japan, and the US, 1970–2015 _Annual growth rate of GDP at constant market prices

Sources: Official national accounts in each country, including author adjustments.

In 2009, at the height of the global financial storm, the growth differentials were 6.6 and 8.3 percentage points with the US and the EU15, respectively. In 2010, simultaneous large-scale fiscal stimulus packages helped major economies rebound strongly, before growth slowed again in 2011. The Asian growth rate thereafter decreased to 5.3% on average per year during 2013–2015, from 7.0% before the global financial crisis (2002–2007). This is mainly due to the onset of deceleration in China’s growth to 7.1% from 11.0% on average in the same periods.4 Plagued by the euro crisis, the EU15 saw their economy shrink by 0.6% from 2011 to 2012 and their recovery to 1.8% 2015 2022 in 2013–2015, whereas the US economy sustained a steady growth of 2.5% in the period 2013–2015. Asia Asia Others 21 %

Asia30 43 %

EU15 15 % EU28 17 %

US 16 %

Other Asia 4%

Others 19 %

47 %

52 %

Asia30 48 %

EU15 13 % EU28 15 %

APO20 25 %

US 14 %

Other Asia 4%

Figure 2 Share of Asia in World GDP in 2015 and Projection for 2022 _Share of GDP using constant PPP

Source: IMF, World Economic Outlook Database, April 2017.

3: The data used in the Databook series includes author adjustments made to better harmonize GDP coverage across countries. See Appendix 1 for the GDP harmonization in this Databook. 4: According to the preliminary estimation by the National Bureau of Statistics of China, the growth rate of Chinese GDP is estimated as 6.7% in 2016 (reported on 24 January 2017), which is the weakest in a quarter century. The annualized growth for the 1st quarter of 2017 is 6.9% to the same quarter in 2016 (reported on 18 April 2017). OECD (2017b) forecasts the Chinese growth is set to edge down further, from 6.6% in 2017 to 6.4% by 2018.

15


It is therefore no surprise that the center of gravity in the global economy is gradually shifting towards Asia. In 2015, the Asian economy contributed 47% (43% for Asia30) of world output, compared with the US and the EU28, each accounting for 16% and 17%, respectively (Figure 2). The IMF (2017) projects the Asian share in world output will

APO20 23 %

3 Economic Growth

continue to rise, reaching 52% (48% for Asia30) by 2022. In contrast, the output shares of each of the US and the EU28 will shrink by a similar extent to 14–15%. To better understand the dynamics of the long-term economic growth within the region, the remainder of this chapter details countries’ diverse development efforts and achievements since 1970, through cross-country level comparisons of GDP and other related performance indicators. To facilitate international level comparisons, harmonized GDP for each of the individual countries is expressed in its equivalent in a common currency unit, customarily in the US dollar, using a set of conversion rates between the individual national currencies. The choices for conversion rates are exchange rate and PPP.

3.1 Economic Scale and Growth Table 1 provides snapshot-level comparisons of Asian countries, based on GDP at current market prices using exchange rates,5 for the six separate years of 1970, 1980, 1990, 2000, 2010, and 2015. By this measure, Japan was the largest economy in Asia until 2010 when China finally overtook Japan’s position to become the second-largest economy in the world next to the US. Japan clearly surged ahead between the 1970 and 1990 comparisons; dwarfing the relative size of all other Asian economies and reducing the US lead from five times to less than twice its economy. The turn of Japan’s fortune came at of the beginning of the 1990s, when the country’s excessive growth years of the late 1980s ended and its descent began. Thereafter, stagnation in Japan combined with vibrant growth in developing Asia result56 Australia 35 Japan ed in the rapid erosion of Japan’s prominence in the re−23 Korea gional economy. The leading position of the four largest −29 Singapore −30 Hong Kong Asian economies (China, Japan, India and Korea) has been −31 UAE −34 Qatar consistent, with their positions rather secure in the past −38 Kuwait three decades. On this measure, the Asia30 was 36% and −41 Turkey −42 Fiji 48% larger than the US and the EU15 in 2015, respectively. −43 Brunei


Comparisons based on exchange rates, however, appear arbitrary as movements in exchange rates can be volatile and subject to short-term or substantial fluctuations of speculative capital flows and government intervention. Furthermore, comparisons based on exchange rates typically underestimate the size of a developing economy and, in turn, the perceived welfare of its residents. The scale of economy ranking changes dramatically when international price differences are properly taken into account.6 Figure 3 shows the extent to which the exchange rates have failed to reflect countries’ price differentials properly, relative to the US, based on the PPP estimates of the 2011 International Comparisons Program (ICP) round, published in April 2014. With the exception of Japan and Australia, exchange rates systematically under-represent the relative purchasing power for all the countries covered in this report. The underestimation is substantial for some, ranging from 23% for Korea to 72% for Pakistan. Thus, the exchange-rate-based GDP considerably underestimates

16

−44 −46 −49 −50 −51 −52 −56 −58 −59 −59 −59 −64 −65 −67 −67 −67 −68 −69 −69 −71 −72

−80 −60 −40 −20 0

Bahrain China ROC Oman Saudi Arabia Malaysia Iran Mongolia Philippines Indonesia Thailand Bhutan Sri Lanka Nepal Cambodia Vietnam India Bangladesh Lao PDR Myanmar Pakistan 20 40 60 %

Figure 3 Price Level Indices of GDP, 2011

_Ratio of PPP to exchange rate (reference country=US)

Sources: Analysis of Main Aggregate rates by United Nations Statistics Division (UNSD) and PPP by World Bank (2014).


Table 1 GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015 _GDP at current market prices, using annual average exchange rate

1970

Australia Turkey

1980

(%)

208 100.0 Japan 93 44.7 China 64 30.5 India 11 5.4 Iran 10 4.9 Pakistan 10 4.8 Indonesia Bangladesh 10 4.7 9.0 4.3 Korea 7.3 3.5 Thailand 6.8 3.3 Philippines 5.8 2.8 ROC 5.4 2.6 Saudi Arabia 3.9 1.9 Malaysia 3.8 1.8 Hong Kong 3.0 1.4 Kuwait Sri Lanka 2.8 1.4 2.7 1.3 Myanmar 1.9 0.9 Singapore 1.2 0.6 Vietnam 1.1 0.5 Nepal 1.1 0.5 UAE 0.8 0.4 Cambodia 0.5 0.3 Qatar 0.4 0.2 Bahrain 0.3 0.1 Oman 0.2 0.1 Fiji 0.2 0.1 Brunei 0.2 0.1 Lao PDR 0.1 0.1 Mongolia 0.1 0.0 Bhutan (regrouped) 358 171.9 APO20 454 218.0 Asia24 464 223.1 Asia30 320 153.7 East Asia 88 42.1 South Asia 35 16.7 ASEAN 30 14.4 ASEAN6 4.8 2.3 CLMV 11 5.1 GCC (reference) 1,076 517.0 US 1,248 599.5 EU15 45 24

21.7 11.7

Japan China India Saudi Arabia Iran Indonesia Korea UAE ROC Thailand Philippines Kuwait Hong Kong Malaysia Pakistan Bangladesh Singapore Qatar Oman Myanmar Brunei Sri Lanka Bahrain Nepal Fiji Vietnam Cambodia Mongolia Lao PDR Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 Australia Turkey

1,748 2,065 2,322 1,530 241 196 188 8.1 257

160.8 190.0 213.6 140.7 22.2 18.0 17.3 0.7 23.6

2,863 263.3 3,321 305.5 173 92

1990

(%)

1,087 100.0 306 28.2 190 17.5 165 15.2 97 9.0 80 7.3 65 6.0 44 4.1 42 3.9 33 3.1 33 3.0 30 2.7 29 2.7 25 2.3 24 2.2 19 1.7 12 1.1 7.9 0.7 6.3 0.6 5.9 0.5 5.0 0.5 4.9 0.5 3.5 0.3 2.6 0.2 1.2 0.1 1.0 0.1 0.7 0.1 0.5 0.0 0.5 0.0 0.1 0.0

16.0 8.5

Japan China India Korea ROC Indonesia Saudi Arabia Iran Thailand Hong Kong UAE Philippines Malaysia Pakistan Singapore Bangladesh Kuwait Oman Sri Lanka Qatar Vietnam Myanmar Bahrain Nepal Brunei Cambodia Mongolia Fiji Lao PDR Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 Australia Turkey

(%)

3,128 100.0 395 12.6 335 10.7 279 8.9 167 5.3 127 4.1 118 3.8 95 3.0 89 2.8 77 2.5 51 1.6 47 1.5 45 1.4 44 1.4 39 1.2 31 1.0 19 0.6 12 0.4 9.4 0.3 7.5 0.2 6.5 0.2 5.6 0.2 4.5 0.1 4.4 0.1 3.4 0.1 1.8 0.1 1.6 0.1 1.4 0.0 1.2 0.0 0.3 0.0 4,530 4,933 5,145 4,047 425 366 350 15 212

144.8 157.7 164.5 129.4 13.6 11.7 11.2 0.5 6.8

5,980 191.1 6,387 204.2 324 204

10.4 6.5

2000 Japan China Korea India ROC Saudi Arabia Hong Kong Indonesia Thailand Iran UAE Singapore Malaysia Philippines Pakistan Bangladesh Kuwait Vietnam Oman Sri Lanka Qatar Bahrain Myanmar Nepal Brunei Cambodia Fiji Lao PDR Mongolia Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

4,888 100.0 1,211 24.8 562 11.5 482 9.9 331 6.8 190 3.9 172 3.5 168 3.4 127 2.6 111 2.3 106 2.2 96 2.0 95 1.9 81 1.7 72 1.5 51 1.1 38 0.8 33 0.7 20 0.4 19 0.4 18 0.4 8.4 0.2 7.8 0.2 6.3 0.1 5.8 0.1 3.7 0.1 1.7 0.0 1.6 0.0 1.4 0.0 0.4 0.0 7,302 8,527 8,908 7,165 631 618 572 46 381

149.4 174.5 182.3 146.6 12.9 12.7 11.7 0.9 7.8

10,285 210.4 9,982 204.2 11,105 227.2 409 8.4 273 5.6

2010 China Japan India Korea Indonesia Saudi Arabia Iran ROC Thailand UAE Malaysia Singapore Hong Kong Philippines Pakistan Qatar Kuwait Vietnam Bangladesh Oman Sri Lanka Myanmar Bahrain Nepal Brunei Cambodia Mongolia Lao PDR Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

6,101 100.0 5,700 93.4 1,671 27.4 1,094 17.9 756 12.4 532 8.7 477 7.8 446 7.3 342 5.6 294 4.8 255 4.2 236 3.9 229 3.7 200 3.3 175 2.9 128 2.1 118 1.9 117 1.9 115 1.9 60 1.0 56 0.9 37 0.6 26 0.4 19 0.3 14 0.2 11 0.2 7.2 0.1 6.9 0.1 3.2 0.1 1.6 0.0 11,916 18,070 19,227 13,577 2,037 1,975 1,802 173 1,157

195.3 296.2 315.2 222.6 33.4 32.4 29.5 2.8 19.0

14,964 245.3 14,619 239.6 16,803 275.4 1,294 21.2 772 12.7

2015 China Japan India Korea Indonesia Saudi Arabia ROC Thailand Iran UAE Hong Kong Singapore Malaysia Philippines Pakistan Vietnam Bangladesh Qatar Kuwait Sri Lanka Oman Myanmar Bahrain Nepal Cambodia Brunei Lao PDR Mongolia Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

11,008 100.0 4,383 39.8 2,108 19.1 1,383 12.6 862 7.8 660 6.0 525 4.8 403 3.7 395 3.6 384 3.5 309 2.8 297 2.7 296 2.7 293 2.7 269 2.4 196 1.8 194 1.8 169 1.5 117 1.1 80 0.7 71 0.6 48 0.4 31 0.3 22 0.2 18 0.2 13 0.1 13 0.1 12 0.1 4.4 0.0 2.1 0.0 12,062 23,133 24,565 17,620 2,675 2,439 2,164 275 1,432

3

109.6 210.2 223.2 160.1 24.3 22.2 19.7 2.5 13.0

18,037 163.9 16,624 151.0 19,280 175.1 1,243 11.3 859 7.8

5: The exchange rates used in this Databook are the adjusted rates, which are called the Analysis of Main Aggregate (UNSD database) rates in the UN Statistics Division’s National Accounts Main Aggregate Database. The AMA rates coincide with the IMF rates (which are mostly the annual average of market or official exchange rates) except for some periods in countries with official fixed exchange rates and high inflation, when there could be a serious disparity between real GDP growth and growth converted to US dollars based on IMF rates. In such cases, the AMA adjusts the IMF-based rates by multiplying the growth rate of the GDP deflator relative to the US. 6: This is because exchange rates embody the trade sector bias (i.e., is more influenced by the prices of traded than non-traded goods and services) and thus do not necessarily succeed in correcting the price differentials among countries. As developing economies tend to have relatively lower wages and, in turn, lower prices for non-traded goods and services, a unit of local currency has greater purchasing power in the local economy than reflected in its exchange rate.

17


Unit: Billions of US dollars. Sources: Official national accounts in each country, including author adjustments. Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

Table 2 GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2015 _GDP at constant market prices, using 2011 PPP, reference year 2015 1970

1980

(%)

2,547 100.0 Japan 977 38.3 India Saudi Arabia 774 30.4 761 29.9 China 472 18.5 Indonesia 408 16.0 Iran UAE 209 8.2 206 8.1 Korea 206 8.1 Philippines 186 7.3 Thailand 153 6.0 ROC 150 5.9 Pakistan 121 4.7 Kuwait 103 4.0 Malaysia 93 3.6 Bangladesh Hong Kong 85 3.4 56 2.2 Vietnam 53 2.1 Singapore 52 2.1 Myanmar 43 1.7 Sri Lanka 32 1.3 Qatar 30 1.2 Brunei 29 1.1 Oman 17 0.7 Nepal 16 0.6 Bahrain 6.0 0.3 Mongolia 4.0 0.2 Lao PDR 4.0 0.1 Fiji 1.0 0.0 Bhutan

(regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15

(regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15

Australia Turkey

3,582 4,046 4,413 2,214 947 588 505 85 487

221.7 250.4 273.1 137.0 58.6 36.4 31.3 5.2 30.1

5,194 321.5 6,422 397.5 293 257

18.2 15.9

Australia Turkey

5,775 6,619 7,733 3,760 1,280 1,167 1,049 118 1,182

226.7 259.9 303.6 147.6 50.3 45.8 41.2 4.6 46.4

7,095 278.6 8,778 344.6 392 382

1990

(%)

1,616 100.0 Japan 727 45.0 India 416 25.8 China 293 18.1 Iran Saudi Arabia 293 18.1 211 13.1 Indonesia Kuwait 149 9.2 115 7.1 Philippines 97 6.0 Thailand 94 5.8 Pakistan 86 5.3 Korea 85 5.3 Bangladesh 57 3.5 ROC 46 2.9 Malaysia 43 2.7 Vietnam Hong Kong 36 2.2 35 2.2 Myanmar 29 1.8 Sri Lanka 22 1.4 Singapore 18 1.1 Qatar 13 0.8 Nepal 12 0.8 Brunei 11 0.7 UAE 10 0.6 Oman 8.0 0.5 Bahrain 4.0 0.2 Mongolia 3.0 0.2 Lao PDR 2.0 0.1 Fiji 0.0 0.0 Bhutan

15.4 15.0

Japan China India Indonesia Saudi Arabia Korea Iran Thailand ROC Pakistan Philippines UAE Malaysia Hong Kong Bangladesh Singapore Vietnam Kuwait Sri Lanka Oman Myanmar Qatar Nepal Brunei Bahrain Mongolia Cambodia Lao PDR Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 Australia Turkey

(%)

4,020 100.0 1,849 46.0 1,677 41.7 866 21.5 728 18.1 531 13.2 528 13.1 397 9.9 338 8.4 301 7.5 250 6.2 214 5.3 187 4.7 164 4.1 139 3.5 111 2.8 95 2.4 92 2.3 66 1.6 65 1.6 59 1.5 37 0.9 27 0.7 23 0.6 19 0.5 11 0.3 9.0 0.2 7.0 0.2 5.0 0.1 1.0 0.0 9,727 11,660 12,811 6,912 2,210 2,005 1,834 171 1,155

242.0 290.1 318.7 172.0 55.0 49.9 45.6 4.2 28.7

9,850 245.1 11,220 279.1 528 635

13.1 15.8

2000 China Japan India Indonesia Korea Saudi Arabia Iran ROC Thailand Pakistan Malaysia UAE Philippines Hong Kong Bangladesh Singapore Vietnam Kuwait Sri Lanka Oman Myanmar Qatar Nepal Bahrain Brunei Cambodia Lao PDR Mongolia Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

4,984 100.0 4,575 91.8 2,853 57.2 1,309 26.3 1,039 20.8 954 19.1 781 15.7 646 13.0 618 12.4 484 9.7 380 7.6 351 7.0 351 7.0 241 4.8 230 4.6 222 4.4 206 4.1 162 3.2 110 2.2 104 2.1 102 2.1 71 1.4 44 0.9 30 0.6 28 0.6 18 0.4 13 0.3 12 0.2 6.0 0.1 2.0 0.0 14,136 19,253 20,907 11,497 3,723 3,246 2,907 339 1,673

283.6 386.3 419.5 230.7 74.7 65.1 58.3 6.8 33.6

13,815 277.2 14,045 281.8 15,946 320.0 748 15.0 909 18.2

2010 China India Japan Indonesia Korea Iran Saudi Arabia ROC Thailand Pakistan Malaysia Philippines UAE Vietnam Bangladesh Singapore Hong Kong Kuwait Qatar Myanmar Sri Lanka Oman Nepal Bahrain Cambodia Brunei Lao PDR Mongolia Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

13,572 100.0 5,842 43.0 4,875 35.9 2,179 16.1 1,601 11.8 1,411 10.4 1,330 9.8 971 7.2 968 7.1 781 5.8 632 4.7 559 4.1 521 3.8 419 3.1 396 2.9 390 2.9 360 2.7 245 1.8 242 1.8 196 1.4 182 1.3 146 1.1 64 0.5 54 0.4 39 0.3 32 0.2 26 0.2 22 0.2 7.0 0.0 5.0 0.0 21,723 35,529 38,083 21,402 7,270 5,439 4,759 680 2,538

160.1 261.8 280.6 157.7 53.6 40.1 35.1 5.0 18.7

16,262 119.8 15,901 117.2 18,293 134.8 1,014 7.5 1,347 9.9

2015 China India Japan Indonesia Korea Saudi Arabia Iran Thailand ROC Pakistan Malaysia Philippines UAE Vietnam Bangladesh Singapore Hong Kong Qatar Kuwait Sri Lanka Myanmar Oman Nepal Bahrain Cambodia Lao PDR Mongolia Brunei Fiji Bhutan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

(%)

19,828 100.0 7,915 39.9 5,119 25.8 2,852 14.4 1,856 9.4 1,716 8.7 1,368 6.9 1,124 5.7 1,102 5.6 954 4.8 817 4.1 745 3.8 661 3.3 560 2.8 538 2.7 477 2.4 416 2.1 323 1.6 296 1.5 246 1.2 180 0.9 178 0.9 77 0.4 64 0.3 55 0.3 40 0.2 36 0.2 32 0.2 8.0 0.0 7.0 0.0 26,305 46,352 49,613 28,358 9,735 6,882 6,047 835 3,237

132.7 233.8 250.2 143.0 49.1 34.7 30.5 4.2 16.3

18,037 16,651 19,184 1,163 1,898

91.0 84.0 96.8 5.9 9.6


Unit: Billions of US dollars (as of 2015). Sources: Official national accounts in each country, including author adjustments. Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

the economic scales in real terms for those countries. By taking into account the international price differentials, PPP rectifies the trade sector bias, and in turn the relative size of economies can be more adequately measured.7 Table 2 repeats the same snapshot level comparisons on real GDP for Asian countries in Table 1, using PPP as conversion rates. By correcting international price differentials, the Asia30 has been expanding rapidly. It was 175%, instead of 36%, larger than the US economy in 2015, having overtaken it in 1974

18


US=100

US=100

280

120

250

100

220

Asia30

160

3

60 EU15 US

100 70

China

80

190

130

US

40 20

APO20

Japan India

East Asia

40 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 4 Regional GDP of Asia and the EU, Relative to the US, 1970–2015

Figure 5 GDP of China, India, and Japan, Relative to the US, 1970–2015



_Indices of GDP at constant market prices, using 2011 PPP


(Figure 4).8 East Asia (China, the ROC, Hong Kong, Japan, Korea, and Mongolia) caught up with the US in 2006 from a low base of 43% in 1970. In contrast, the EU15 has been experiencing a gradual relative decline in economic size, from 124% of the US economy in 1970 to a low of 92% in 2015. Based on GDP using constant PPP, the weight of the world economy is even more tilted toward Asia than portrayed by GDP using exchange rates. This reflects the fact that nearly all Asian countries increase in relative size after international price differentials have been properly taken into account. The relative size of China’s economy in 2015 was 3.9 times that of Japan, compared with 2.5 times when exchange rates are used in Table 1. Considering that the Chinese economy was only 26% that of Japan and 57% that of India in 1970, represents remarkable growth. China overtook Japan after 1999 to become the leading economy in Asia as shown in Figure 5.9 On this measure, Figure 5 also demonstrates that Chinese GDP overtook the US as the world’s largest economy in 2013, although it was only 8% that of the US in 1970. The level and the timing to overcome should not be taken as precise numbers,10 but they may provide a good basis for assessing the relative production size of these two economies. Based on the estimates in Maddison (2007), China was the largest producer in the world as of 1880. For the first time in more than 130 years, China comes back to this position.

7: It is therefore important to note that any international GDP comparisons are sensitive not only to revisions in national accounts but also to revisions in multilateral PPPs, which are currently benchmarked every six years. PPPs for most Asian countries have been revised downward, compared with what they would have been by extrapolating the 2005 benchmark PPP (see Box 1). This has the effect of raising the relative sizes of these economies against the base economy. 8: This compares with the findings in Databook 2013, which were based on the 2005 benchmark PPP, that the economic size of the Asia30 overtook the US in 1988. 9: The shift of the benchmark year PPP estimates from 2005 to 2011 has the effect of bringing forward the year when China overtook Japan in relative GDP to 1999, from 2002 in Databook 2013. 10: BBC News: Is China's economy really the largest in the world?, 16 December 2014.

19


Given that PPP for India has been revised by –24% in the 2011 ICP round (see Box 1), the effects have been to raise the relative size of India. Compared to Japan, the Indian economy has been increasing from 45% in 1970 to 155% in 2015, surpassing Japan and replacing it as the second largest economy in Asia in 2009. In 2015, the total GDP of the three countries, which are counted as the largest economies in Asia, was larger than the US economy by 82%.

3 Economic Growth

Box 1

PPP in the 2011 ICP Round

Purchasing power parities (PPPs) are indispensable inputs into economic research and policy analysis involving cross-country comparisons of macroeconomic aggregates. They affect a double conversion of macroeconomic measures, estimated in national currencies and price levels, into comparable cross-country volume measures. These are expressed in a common currency and at a uniform price level. PPPs are price relatives that show the ratio of the prices in national currencies of single or composite goods and services in different countries. They are compiled within the International Comparisons Program (ICP). Comparisons are made from the expenditure side of GDP. To this end, the ICP compiles PPPs by holding worldwide surveys at regular intervals (currently, every six years) to collect comparable price and expenditure data for the whole range of final goods and services that make up the final expenditures on GDP. In April 2014, the new benchmark PPP estimates were published by the ICP 2011 round. For a number of methodological improvements, see Eurostat-OECD (2012) and World Bank (2014). Chapter 3 mainly provides the cross-country comparison of economic volumes. To obtain comparable volume measures, the Databook uses the constant PPP approach, which relies not on a time series of PPPs, but on one of the benchmark estimates. The Databook has used the new benchmark estimates by the ICP 2011 round since the 2015 publication. The use of this approach creates national series for volumes at the prices of a common reference year (i.e., 2014), and deflates these by the PPP for a fixed year (i.e., 2011). It is inevitable that they will be compared with the results of the previous round in 2005, which has provided the benchmark estimate for the past Databook series in 2009– 2013. Figure B1 shows the revisions of PPPs in Asian countries at the 2011 ICP round, in comparison with the 2005 ICP round. The 2011 benchmark PPP for most of the Asian countries is lower than suggested by their extrapolated equivalents from the 2005 benchmark, with a difference ranging from +3% for Korea to –47% for Myanmar. With the exception of Singapore, it is observed that revisions for the more mature economies are much smaller (ranging within ±4%) than those for the rapidly developing economies (with downward revisions greater than 10%). Therefore, the impact of the PPP revisions is to raise the relative size of Asian economies, moving them closer to the level of the more mature economies. More specifically, the PPP revisions for India and China are –24% and –16%, respectively. As a result, the relative positions of India and China have improved considerably in cross-country level comparisons after PPP revisions at the 2011 ICP round.

3 1 1 −1 −4 −4 −13 −14 −16 −16 −18 −21 −22 −23 −24 −27 −28 −28 −29 −31 −31 −34 −35 −36 −37 −39 −39 −40 −41 −45 −45 −47

−50

−40

−30

−20

−10

0

Korea Australia Hong Kong Japan ROC Turkey Cambodia Bhutan Singapore China Iran Vietnam Bangladesh Malaysia India Brunei UAE Philippines Thailand Sri Lanka Nepal Pakistan Lao PDR Mongolia Fiji Saudi Arabia Qatar Bahrain Oman Kuwait Indonesia Myanmar

10 %

Figure B1 Revisions of PPP for GDP by the 2011 ICP Round

_Ratio of the 2011 ICP PPP to the 2005 ICP PPP (extrapolated for 2011)

Source: World Bank, World Development Indicators 2014.


These revisions by the 2005 ICP round have a property to partly offset the past upward revisions by the 2005 ICP round for many Asian countries. The 2005 benchmark PPP for most of the Asian countries were upwardly revised compared to their extrapolated equivalents from the 1993 benchmark estimates that had been used in the Databook 2008. For example, the PPP estimates were upwardly revised by 55% and 65% (thus the internationally comparable measures of GDP in 2005 were reduced by 36% and 40%) for India and China, respectively. Singapore is an exceptional country, in which the PPP has been downwardly revised (thus the relative size of the economy has been upwardly revised) by both of the revisions of the ICP 2005 and 2011 rounds. The PPP for Singaporean GDP was revised by –29% and by –16% in the ICP 2005 and 2011 rounds, respectively. continued on next page >

20


> continued from previous page

Based on the constant PPP approach, the revision by the ICP 2011 round advanced the years when the Singapore economy has surpassed Japan and the US to 1980 (from 1993) and 1992 (from 2004), respectively, as a measure of per capita GDP. It may require further examination if this revision provides an appropriate view. Generally speaking, the cross-country level comparison has to face a much larger opportunity to be revised, compared to the cross-country growth comparison. The readers should bear in mind these circumstances.

3 Figure 6 shows the rapid expansion of the relative size of the South Asian economy (consisting of Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka), 81% of which was accounted for by India in 2015. The ASEAN also showed vigor in their catch-up effort. They were on par with the South Asian economy in 1996–1997 before the setback caused by the Asian financial crisis of 1997–1998 took hold, setting them on a lower growth path, once again opening up a divergence. In contrast, the progress of GCC11 countries lagged for more than two decades. Only in the past decade has it picked up and brought the relative size of the country group back to its previous peak of the early 1980s.12

US=100

60 50

South Asia

40

ASEAN

30 20

ASEAN6 GCC

10 CLMV

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 6 Regional GDP of South Asia, ASEAN, CLMV, and GCC, Relative to the US, 1970–2015


Sources: Official national accounts in each country, including auPerformance of each country is also transthor adjustments. formed when economic growth is used as a yardstick. Table 3 presents cross-country comparisons of real GDP growth in Asia since 1990. The ranking varies from period to period and the economic giants no longer take precedence in the ranking. In fact, small developing Asian countries are equally capable of exhibiting exuberant growth.13 As labor costs are edging up in China (see Box 4, p. 53), the workshop of the world has started shifting its location to the neighboring countries such as Cambodia, the Lao PDR, Myanmar, and Vietnam, called CLMV. They are clearly the faster growing group among the ASEAN countries, at 6.4% on average per year compared with 4.8% managed by the ASEAN6 in the period 1990–2015.

11: GCC consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE. These GCC countries display economic characteristics very different from those of other Asian economies due to their profound reliance on the oil and energy sector. In 2015, these countries account for about 33% of the world’s crude oil reserves and possess at least 21% of the global natural gas reserves (GCC Secretariat General, 2017). 12: In interpreting the results in this report, one must bear in mind that conventional GDP tends to overstate the income of these oilexporting countries since it does not account for the depletion of natural resource stock, and in turn a large part of their GDP may not be sustainable. Besides, GDP growth can underestimate the growth of real income available to the country brought about by a favorable change in terms of trade, and vice versa. For an oil-exporting country, the growth wedge of the two measures could be significant in the face of volatile oil prices. See Chapter 7. 13: In comparison of economic growths among Asian countries, Myanmar was ranked as the top position (12.1%) and the second position (10.7%) in the periods of the first and the second half of the 2000s, respectively, in Databook 2016. However, some questions have been raised about the reliability of Myanmar’s official system of national accounts since the late 1990s. This edition of Databook attempts to revise the past economic performance based on the industry-level examinations in Nomura and Shirane (2016). See Box 5 (p. 56) for the details of this revision.

21


At the other end of the table, over the past two decades (1990–2015) Japan has been struggling consistently at the bottom with an average growth of 1.0% per year, compared with Asia30’s 5.4% and EU15’s 1.6%. During this period, only three Asian countries – Brunei, Fiji, and Japan – grew slower than the US (2.4%). The divergence of growth performance between the Asian countries on the one hand

3 Economic Growth

Table 3 GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015 _Average annual growth rate of GDP at constant market prices

1990–1995

1995–2000

China Malaysia Kuwait Singapore Vietnam Thailand Korea Indonesia ROC Cambodia Lao PDR Oman Pakistan Sri Lanka Bahrain Hong Kong Bangladesh India Myanmar Nepal Iran UAE Bhutan Brunei Philippines Saudi Arabia Fiji Qatar Japan Mongolia (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15

11.6 9.3 9.2 8.3 8.1 8.1 8.1 7.5 7.2 6.6 6.2 5.7 5.5 5.3 5.3 5.2 5.0 5.0 4.9 4.9 3.7 3.6 3.4 3.1 2.8 2.8 2.7 2.3 1.5 −1.8

Australia Turkey

3.2 3.2

4.4 5.7 5.5 5.6 5.1 7.2 7.3 6.9 3.8 2.6 1.6

Qatar China Vietnam Cambodia UAE Myanmar ROC Bhutan India Lao PDR Singapore Korea Bangladesh Malaysia Sri Lanka Nepal Bahrain Iran Pakistan Philippines Oman Mongolia Hong Kong Saudi Arabia Kuwait Fiji Brunei Japan Thailand Indonesia (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

10.6 8.3 7.3 7.2 6.3 6.0 5.8 5.7 5.7 5.5 5.5 5.3 5.1 4.9 4.9 4.8 4.2 4.1 4.0 3.9 3.7 3.6 2.6 2.6 2.1 2.0 1.3 1.1 0.7 0.7 3.1 4.3 4.3 4.6 5.4 2.4 1.9 6.8 3.6 4.2 2.9 2.9 3.8 4.0

2000–2005 China Cambodia Vietnam Qatar Bhutan Kuwait Iran India Myanmar Mongolia Lao PDR Bahrain Pakistan UAE Thailand Malaysia Bangladesh Singapore Korea Indonesia Philippines Hong Kong Saudi Arabia Sri Lanka ROC Nepal Brunei Fiji Japan Oman (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

9.3 8.8 8.0 8.0 7.6 7.2 6.9 6.5 6.4 6.3 6.2 5.9 5.9 5.4 5.3 5.2 5.0 4.8 4.6 4.6 4.5 4.1 4.0 4.0 4.0 3.1 2.1 2.0 1.2 1.0 4.2 5.7 5.6 5.6 6.2 5.1 4.8 7.5 4.6 2.5 1.8 1.9 3.4 4.7

2005–2010 Qatar China Bhutan Lao PDR India Myanmar Cambodia Singapore Mongolia Sri Lanka Vietnam Bangladesh Oman Indonesia Bahrain Malaysia Iran Philippines Nepal ROC Korea Hong Kong Thailand Pakistan Saudi Arabia UAE Kuwait Fiji Brunei Japan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

16.6 10.7 9.1 7.8 7.8 6.5 6.5 6.5 6.4 6.2 6.2 5.9 5.7 5.6 5.4 5.0 5.0 4.8 4.4 4.2 4.0 3.8 3.7 3.7 2.7 2.5 1.2 0.7 0.7 0.1 4.4 6.6 6.4 6.8 7.1 5.2 5.0 6.4 3.7 0.8 0.7 0.9 2.7 3.2

2010–2015 Mongolia Lao PDR China Cambodia Bangladesh India Sri Lanka Vietnam Qatar Philippines Bhutan Indonesia Malaysia Saudi Arabia UAE Singapore Pakistan Oman Kuwait Nepal Bahrain Fiji Thailand Korea Hong Kong ROC Japan Brunei Iran Myanmar (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

9.8 8.4 7.6 7.0 6.1 6.1 6.0 5.8 5.8 5.7 5.4 5.4 5.2 5.1 4.7 4.0 4.0 4.0 3.7 3.6 3.6 3.6 3.0 3.0 2.9 2.5 1.0 −0.1 −0.6 −1.7 3.8 5.3 5.3 5.6 5.8 4.7 4.8 4.1 4.9 2.1 0.9 1.0 2.7 6.9

1990–2015 China Qatar Cambodia Vietnam Lao PDR Bhutan India Malaysia Singapore Bangladesh Sri Lanka Korea Bahrain Mongolia Indonesia ROC Kuwait Pakistan UAE Myanmar Philippines Thailand Nepal Oman Iran Hong Kong Saudi Arabia Fiji Brunei Japan (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLMV GCC (reference) US EU15 EU28 Australia Turkey

9.5 8.7 7.2 7.1 6.8 6.3 6.2 5.9 5.8 5.4 5.3 5.0 4.9 4.9 4.8 4.7 4.7 4.6 4.5 4.4 4.4 4.2 4.2 4.0 3.8 3.7 3.4 2.2 1.4 1.0 4.0 5.5 5.4 5.6 5.9 4.9 4.8 6.4 4.1 2.4 1.6 1.6 3.2 4.4


Unit: Percentage. Sources: Official national accounts in each country, including author adjustments. Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

and the US and the EU15 on the other was even more pronounced if focusing on the most recent years, with the Asia30 growing at 5.3% on average per annum, compared with 2.1% in the US and 0.9% in the EU15 in the period 2010–2015. The change of guards in Asia is clearly illustrated in Figure 7. While Japan was the standard-bearer in yesteryears in the left chart of Figure 7, China and India have emerged as the driving force propelling Asia forward since 1990. Their growth accounts for 38% and 14% of regional growth, respectively, in

22


1970–1990 Japan China India Indonesia Saudi Arabia Korea Iran Thailand ROC UAE Pakistan Philippines Malaysia Hong Kong Singapore Oman Vietnam Bangladesh Sri Lanka Myanmar Qatar Brunei Nepal Bahrain Mongolia Cambodia Lao PDR Fiji Bhutan Kuwait

1990–2000 29.0

16.0 10.6 7.8 6.9 5.0 3.8 3.3 3.3 3.0 2.3 1.8 1.7 1.5 1.0 0.7 0.6 0.5 0.4 0.4 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.0 0.0 −0.7

−10

0

10

20

30 %

China India Japan Korea Indonesia ROC Iran Thailand Saudi Arabia Malaysia Pakistan UAE Singapore Vietnam Philippines Bangladesh Hong Kong Kuwait Sri Lanka Myanmar Oman Qatar Nepal Bahrain Cambodia Brunei Lao PDR Fiji Bhutan Mongolia

2000–2010 38.2

14.0 7.1 6.3 6.1 3.8 3.1 3.0 2.9 2.4 2.3 1.6 1.4 1.3 1.2 1.1 1.0 0.9 0.5 0.5 0.5 0.4 0.2 0.1 0.1 0.1 0.1 0.0 0.0 0.0

0

10

20

30

40 %

China India Indonesia Iran Korea Saudi Arabia Thailand Japan ROC Pakistan Malaysia Vietnam Philippines UAE Bangladesh Singapore Qatar Hong Kong Kuwait Myanmar Sri Lanka Oman Bahrain Cambodia Nepal Lao PDR Mongolia Brunei Bhutan Fiji

2010–2015 49.3

17.2 5.1 3.9 3.4 2.2 2.2 2.0 1.9 1.8 1.5 1.3 1.2 1.1 1.0 1.0 0.9 0.7 0.6 0.5 0.4 0.2 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.0

0

China India 17.6 Indonesia 5.9 Saudi Arabia 3.4 Korea 2.2 Japan 2.1 1.6 Malaysia 1.6 Philippines 1.5 Pakistan 1.4 Thailand Bangladesh 1.2 UAE 1.2 Vietnam 1.2 ROC 1.2 Singapore 0.8 0.7 Qatar 0.6 Sri Lanka 0.5 Hong Kong 0.5 Kuwait Oman 0.3 0.1 Cambodia 0.1 Mongolia 0.1 Lao PDR 0.1 Nepal 0.1 Bahrain 0.0 Bhutan 0.0 Fiji 0.0 Brunei Myanmar −0.2 Iran −0.3

10 20 30 40 50 %

54.5

3

−10 0 10 20 30 40 50 60 %

Figure 7 Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, 20002010, and 2010–2015

_Contribution share to the growth of gross regional products (growth rate of Asia30=100) Sources: Official national accounts in each country, including author adjustments. Note: The starting period for Cambodia is 1987.

the 1990s. In the recent period 2010–2015, the growth in China and India accounts for more than twothirds of regional growth (55% and 18%, respectively).14 Indonesia became the third engine of Asian growth (5.9%), followed by Saudi Arabia (3.4%).

14: The growth in Chinese manufacturing sector explains about one-third of the China’s contribution to regional growth (22 percentage points of 64%) in the period 2000–2015. See Figure 85 in Section 6.2 (p. 111) for the industry origins of regional growth.

23


It has been a subject of much debate whether the Asian economy has decoupled from the US and the EU15. If it has, the world economy would be substantially less volatile. Figures 8 and 9 compare the correlation coefficients of growth rates among countries in the 1990s and the period from 2000 to 2015, respectively. Countries are grouped by region. Overall, the fortunes of the reference countries have become increasingly tied to Asia in a pro-cyclical manner. It is interesting to note that China’s correlation with the US and the EU15 has moved from negative to positive. Correlation among the East Asian countries (Group 1) has been strengthened over time and their correlation with the US, the EU15, and the ASEAN countries (Group 3) has strengthened as well. In the South Asian countries (Group 2), their correlation with the US and the EU15 has weakened, although the correlation with ASEAN has grown stronger. Therefore, comparisons of the correlation coefficients of growth between the two periods lend support to an increase in business cycle synchronicity, but in the South Asian countries.

3 Economic Growth

China (CHN) Hong Kong (HKG) Japan (JPN) Korea (KOR) Mongolia (MGL) ROC (ROC) Bangladesh (BAN) Bhutan (BTN) India (IND) Iran (IRN) Nepal (NEP) Pakistan (PAK) Sri Lanka (SRI) Brunei (BRN) Cambodia (CAM) Fiji (FIJ) Indonesia (IDN) Lao PDR (LAO) Malaysia (MAL) Myanmar (MYA) Philippines (PHL) Singapore (SIN) Thailand (THA) Vietnam (VIE) Bahrain (BHR) Kuwait (KWT) Oman (OMN) Qatar (QAT) Saudi Arabia (SAU) UAE (UAE) Australia (AUS) Turkey (TUR) EU15 US

1.0 0.5 1.0 −0.1 0.5 1.0 0.2 0.8 0.6 1.0 −0.6 −0.5 −0.2 −0.2 1.0 0.5 0.8 0.4 0.7 −0.7 1.0 0.4 −0.0 0.1 −0.1 0.1 −0.2 1.0 −0.3 −0.3 −0.3 −0.1 0.8 −0.5 0.3 1.0 0.1 −0.4 −0.3 −0.1 0.6 −0.4 0.6 0.8 1.0 0.0 0.1 0.4 −0.1 −0.4 0.0 0.5 −0.4 −0.1 1.0 0.1 0.1 −0.1 0.2 −0.2 −0.1 0.3 −0.1 0.2 0.4 1.0 0.4 0.3 0.4 0.4 −0.5 0.7 0.4 −0.0 0.1 0.3 −0.1 1.0 0.3 0.3 −0.1 0.1 −0.1 −0.0 −0.3 −0.2 −0.3 −0.3 0.2 −0.6 1.0 0.3 0.4 0.5 0.6 −0.3 0.6 0.3 0.0 0.2 0.3 0.0 0.9 −0.5 1.0 −0.2 0.4 0.2 0.7 0.2 0.4 −0.3 0.3 0.0 −0.1 −0.0 0.3 −0.1 0.5 1.0 0.3 −0.1 −0.4 0.1 0.1 0.1 0.4 0.5 0.8 −0.0 0.2 0.4 −0.5 0.4 0.2 1.0 0.5 0.9 0.7 0.9 −0.4 0.8 0.1 −0.3 −0.2 0.0 0.1 0.5 0.2 0.5 0.3 −0.0 1.0 0.6 0.4 0.3 0.2 0.1 0.2 0.5 0.3 0.4 −0.1 −0.2 0.2 0.3 0.3 −0.1 0.1 0.5 1.0 0.5 0.9 0.7 0.9 −0.4 0.7 0.1 −0.3 −0.2 0.1 0.1 0.4 0.2 0.5 0.3 0.0 1.0 0.5 1.0 0.2 0.3 −0.1 0.1 0.1 −0.0 0.5 0.5 0.3 0.3 0.2 0.2 0.1 0.3 0.4 0.3 0.1 0.5 0.1 1.0 0.1 0.4 0.3 0.4 0.5 −0.1 0.3 0.3 0.4 −0.1 0.2 −0.2 0.3 −0.1 0.1 0.1 0.4 0.6 0.5 0.3 1.0 0.6 0.9 0.4 0.8 −0.3 0.6 0.0 −0.2 −0.1 −0.0 0.2 0.1 0.5 0.2 0.3 0.0 0.9 0.5 0.9 0.3 0.6 1.0 0.6 0.7 0.5 0.8 −0.6 0.8 0.2 −0.3 −0.0 0.2 0.3 0.7 −0.0 0.8 0.3 0.3 0.8 0.3 0.8 0.1 0.1 0.7 1.0 0.5 0.3 0.4 0.1 0.1 0.0 0.7 0.2 0.4 −0.0 −0.1 0.2 0.2 0.2 −0.3 0.1 0.5 0.9 0.4 0.2 0.7 0.4 0.3 1.0 0.3 0.1 −0.2 −0.1 −0.6 0.0 0.3 −0.3 −0.2 0.4 0.8 −0.0 0.3 −0.2 −0.4 −0.0 0.0 −0.2 0.1 0.1 −0.2 0.1 0.2 −0.1 1.0 0.6 0.1 −0.4 −0.2 −0.1 0.0 0.6 0.4 0.4 0.0 0.1 0.3 0.1 0.1 −0.2 0.5 −0.0 0.5 0.0 0.7 0.1 0.2 0.1 0.4 0.4 1.0 0.5 0.5 0.3 0.1 −0.7 0.5 0.1 −0.4 −0.6 0.1 −0.2 0.3 0.4 0.1 −0.2 −0.5 0.4 0.4 0.4 0.3 −0.1 0.4 0.3 0.3 0.3 0.3 1.0 −0.3 −0.1 −0.2 −0.4 0.4 −0.4 −0.1 0.4 −0.0 −0.4 −0.5 −0.3 0.1 −0.5 −0.1 −0.3 −0.3 0.2 −0.2 0.2 0.1 −0.2 −0.7 0.1 −0.2 0.2 0.2 −0.2 0.2 0.5 −0.0 −0.5 0.3 −0.3 −0.6 −0.8 0.5 −0.4 0.3 −0.2 0.1 −0.1 −0.6 0.1 −0.3 0.1 −0.2 −0.5 −0.2 0.0 −0.3 −0.0 −0.4 0.5 −0.2 0.3 0.4 0.3 0.6 −0.2 0.1 0.5 0.3 −0.1 −0.3 −0.1 0.2 0.1 0.5 −0.1 0.2 0.6 0.3 0.5 0.6 0.3 −0.1 0.4 −0.6 0.0 0.0 0.2 −0.5 −0.7 −0.5 0.5 −0.6 0.4 0.7 0.7 −0.4 0.0 −0.3 0.0 −0.4 −0.4 0.5 −0.4 0.3 −0.4 0.2 0.3 −0.2 −0.4 0.4 0.0 0.6 −0.3 0.1 0.1 0.3 −0.2 −0.2 −0.2 0.5 −0.1 −0.1 0.3 0.2 −0.0 0.3 −0.2 −0.5 −0.4 0.1 0.3 0.1 0.2 0.2 0.0 −0.1 0.5 0.6 0.3 0.6 −0.7 −0.3 0.1 −0.0 0.7 −0.4 −0.3 0.5 0.1 −0.2 −0.4 −0.2 −0.2 0.0 0.5 −0.2 −0.4 −0.1 −0.3 0.2 0.0 −0.3 −0.5 −0.3 −0.7 −0.4 −0.4 −0.2 −0.3 −0.5 −0.3 0.7 −0.6 0.3 0.8 0.6 −0.2 −0.1 −0.3 −0.0 −0.3 0.1 0.4 −0.4 0.3 −0.4 0.6 0.4 −0.2 −0.5 0.1 −0.2 0.5 −0.4 If greater than 0.55

1.0 0.0 1.0 0.4 −0.2 0.4 −0.7 0.3 0.1 0.3 0.1 0.5 −0.6

US

TUR

EU15

AUS

UAE

Group 5 SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4 PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

Group 3 PAK

NEP

IRN

IND

BTN

BAN

ROC

Group 2 MGL

KOR

JPN

HKG

CHN

Group 1

1.0 0.0 1.0 0.0 0.1 1.0 0.7 −0.1 −0.3 1.0 0.5 0.7 −0.1 0.4 1.0

If less than −0.55

Figure 8 Correlation of GDP Growth, 1990–2000 _Correlation of GDP growth at constant market prices


3.2 Catching Up in Per Capita GDP


Asia is the most populous region in the world. In 2015, the population of Asia accounted for 60% of the world’s population (56% for Asia30), with China and India alone accounting for more than onethird (Figure 10). In addition, there is a significant difference in the population among Asian economies, as Table 4 shows. Seven countries’ populations were over 100 million in 2015 (the Philippine population reached 100 million in 2015), but the populations are less than 10 million in 12 economies of the Asia30.15 Performance comparisons based on the whole-economy GDP in Section 3.1 do not take into account the population and can in turn exaggerate the wellbeing of countries with large populations. Based on per capita GDP, which adjusts for the differences in population, China and India, two rising giants in the Asian economy, remain substantially less well-off in light of the US standard. Conversely, the Asian Tigers proliferate. Table 5 presents cross-country comparisons of per capita current-price GDP, using exchange rates as conversion rates. However, given the volatile nature of exchange rates, snapshot comparisons as 15: In Myanmar the first census in three decades was conducted between March 30 and April 10, 2014. This showed that the total population was 51 million, which was considerably below the official estimate of 61 million.

24


China (CHN) Hong Kong (HKG) Japan (JPN) Korea (KOR) Mongolia (MGL) ROC (ROC) Bangladesh (BAN) Bhutan (BTN) India (IND) Iran (IRN) Nepal (NEP) Pakistan (PAK) Sri Lanka (SRI) Brunei (BRN) Cambodia (CAM) Fiji (FIJ) Indonesia (IDN) Lao PDR (LAO) Malaysia (MAL) Myanmar (MYA) Philippines (PHL) Singapore (SIN) Thailand (THA) Vietnam (VIE) Bahrain (BHR) Kuwait (KWT) Oman (OMN) Qatar (QAT) Saudi Arabia (SAU) UAE (UAE) Australia (AUS) Turkey (TUR) EU15 US

1.0 0.6 1.0 0.2 0.7 1.0 0.4 0.5 0.6 1.0 0.1 0.5 0.4 0.1 1.0 0.5 0.8 0.7 0.7 0.4 1.0 0.3 0.4 0.2 −0.2 0.4 0.1 1.0 0.6 0.2 0.1 0.6 −0.2 0.5 0.0 1.0 0.4 0.4 0.4 0.1 −0.2 0.4 0.3 0.2 1.0 0.7 0.4 0.1 0.7 −0.2 0.5 −0.4 0.6 0.2 1.0 0.4 0.1 −0.1 −0.1 0.4 0.2 0.1 0.1 0.0 0.0 1.0 0.3 0.6 0.3 0.2 0.2 0.3 0.1 −0.1 0.4 0.3 −0.1 1.0 0.3 0.5 0.3 0.0 0.5 0.5 0.5 0.1 0.2 −0.2 0.4 0.1 1.0 0.3 0.3 0.3 0.6 0.2 0.4 −0.2 0.4 −0.2 0.6 −0.1 0.1 0.1 1.0 0.5 0.8 0.7 0.4 0.5 0.5 0.4 0.1 0.3 0.3 −0.1 0.7 0.2 0.3 1.0 −0.5 0.2 0.4 0.2 0.3 0.2 −0.1 −0.4 −0.2 −0.2 −0.3 0.0 −0.1 −0.1 0.0 1.0 0.4 0.5 0.3 0.1 0.6 0.5 0.7 0.2 0.2 −0.2 0.6 −0.1 0.8 −0.1 0.3 −0.2 1.0 −0.1 0.0 0.0 −0.2 −0.1 −0.0 0.5 0.1 0.2 −0.4 0.1 −0.1 0.4 −0.4 −0.1 0.1 0.4 1.0 0.3 0.7 0.8 0.6 0.5 0.9 0.2 0.2 0.3 0.2 0.1 0.2 0.6 0.3 0.6 0.3 0.6 0.1 1.0 0.3 0.3 0.2 0.5 −0.3 0.5 −0.2 0.4 0.3 0.6 −0.2 0.1 −0.2 0.1 0.2 0.0 −0.1 −0.0 0.4 1.0 0.1 0.6 0.8 0.3 0.4 0.6 0.4 0.0 0.5 −0.2 0.1 0.2 0.5 −0.1 0.5 0.5 0.5 0.3 0.8 0.0 1.0 0.6 0.9 0.7 0.6 0.4 0.9 0.2 0.4 0.5 0.4 0.3 0.3 0.6 0.3 0.5 0.1 0.6 0.1 0.8 0.4 0.7 1.0 0.3 0.5 0.7 0.6 0.2 0.6 −0.1 0.3 0.3 0.3 0.0 0.3 0.4 0.5 0.5 0.0 0.2 −0.2 0.6 −0.0 0.5 0.6 1.0 0.4 0.5 0.4 0.5 −0.1 0.4 −0.2 0.3 0.3 0.7 −0.2 0.7 −0.1 0.4 0.7 −0.2 −0.2 −0.3 0.3 0.5 −0.0 0.3 0.4 1.0 0.7 0.5 0.4 0.4 0.1 0.4 0.1 0.3 0.4 0.5 0.3 0.6 0.1 0.1 0.7 −0.3 0.2 −0.3 0.4 0.3 0.3 0.3 0.4 0.6 1.0 0.3 0.5 0.4 0.1 0.6 0.3 0.2 0.1 −0.0 0.3 0.2 0.6 0.4 0.4 0.7 −0.1 0.2 −0.2 0.4 −0.1 0.2 0.3 0.5 0.6 0.5 1.0 −0.0 −0.2 −0.2 −0.3 −0.1 −0.4 0.5 −0.2 0.1 −0.6 −0.1 −0.4 0.1 −0.4 −0.2 −0.2 0.3 0.4 −0.3 −0.3 0.1 −0.2 −0.3 −0.5 −0.2 −0.5 1.0 0.7 0.5 0.0 0.3 0.3 0.4 0.2 0.3 0.2 0.4 0.3 0.1 0.4 0.2 0.2 −0.1 0.5 0.0 0.2 0.2 0.1 0.6 0.1 0.0 0.3 0.0 0.3 1.0 0.1 0.5 0.4 −0.2 0.6 0.3 0.3 −0.2 −0.0 −0.1 0.2 0.2 0.5 0.1 0.4 0.1 0.5 0.1 0.5 −0.1 0.4 0.4 0.2 0.2 0.1 0.7 −0.3 0.1 0.2 0.7 0.6 0.2 0.7 0.4 0.3 −0.2 0.1 0.1 0.1 0.6 0.4 0.4 0.8 0.3 0.2 −0.1 0.5 −0.2 0.5 0.4 0.5 0.4 0.4 0.8 −0.3 0.2 0.4 0.4 0.3 0.4 0.3 0.2 −0.0 0.4 0.0 0.6 −0.1 0.4 −0.1 0.7 0.5 −0.1 −0.2 −0.4 0.1 0.0 −0.0 0.2 0.4 0.5 0.4 0.7 −0.6 0.1 0.2 0.8 0.7 0.4 0.7 0.7 0.3 0.0 0.2 0.1 0.3 0.4 0.6 0.2 0.6 0.4 0.6 0.3 0.8 0.1 0.6 0.7 0.4 0.3 0.2 0.5 −0.4 0.2 0.3 0.7 0.8 0.7 0.4 0.6 0.3 0.4 0.2 0.4 −0.3 0.4 0.1 0.5 0.8 0.3 0.1 −0.0 0.6 0.4 0.5 0.5 0.4 0.5 0.4 0.5 −0.3 0.2 0.1 0.8 0.9 0.5 0.5 0.7 0.2 0.0 0.3 0.2 −0.2 0.6 0.3 0.3 0.8 0.4 0.2 −0.0 0.8 0.2 0.7 0.6 0.6 0.6 0.4 0.6 −0.4 −0.0 If greater than 0.55

US

TUR

EU15

AUS

UAE

Group 5 SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4 PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

PAK

Group 3

NEP

IRN

IND

BTN

BAN

ROC

Group 2 MGL

KOR

JPN

HKG

CHN

Group 1

3

1.0 0.7 0.2 0.6 0.3 0.5

1.0 0.5 0.6 0.6 0.8

1.0 0.2 1.0 0.6 0.5 1.0 0.5 0.7 0.8 1.0

If less than −0.55

Figure 9 Correlation of GDP Growth, 2000–2015 _Correlation of GDP growth at constant market prices


2015

Others 29 %

EU28 7%

Asia 60 % APO20 36 %

Asia30 EU15 56 % 6 % US 4 % Other Asia 4%

Figure 10 Share of Asian Population in the World, 2015 Source: IMF, World Economic Outlook Database, April 2017.

16: Singapore’s population comprises not only Singaporean citizens but also non-citizens who have been granted permanent residence in Singapore as well as non-permanent residents such as employment pass holders, work permit holders, and student pass holders. It is known that many workers and students commute to Singapore from outside the country every day. According to the most recent census, the share of Singaporean citizens with respect to total population was 74% in 2000, the share of permanent residents who are not Singaporean citizens was 7%, and the share of non-permanent residents was 19%.

25


those presented in Table 5 can appear arbitrary. Rather, long-term trends of nominal per capita GDP provide a better guide of relative movements. Based on this measure, Japan closed in on the US level in the late 1980s and peaked in 1995, reflecting the strong yen of 94.1 yen per dollar, as shown in Figure 11. However, it is 40% below the US level in 2015, in which the average annual exchange rate is 121.0 yen per dollar. Figure 12 shows comparisons among the Asian Tigers. Singapore and Hong Kong have been moving closely with one another for three and a half decades until the mid-2000s, when Singapore overtook Hong Kong.16 Hong Kong’s per capita GDP peaked in 1997, the year

3 Economic Growth

Table 4 Population, 1970, 1980, 1990, 2000, 2010, and 2015

1970

(%)

1980

(%)

China India Indonesia Japan Bangladesh Pakistan Vietnam Philippines Thailand Korea Iran Myanmar ROC Sri Lanka Nepal Malaysia Cambodia Hong Kong Lao PDR Singapore Mongolia Fiji Bhutan

829.9 553.9 116.1 104.7 71.2 60.6 42.7 36.7 34.4 32.2 28.4 27.3 14.8 12.5 11.3 10.9 6.77 3.96 2.50 2.07 1.25 0.52 0.29

41.2 27.5 5.8 5.2 3.5 3.0 2.1 1.8 1.7 1.6 1.4 1.4 0.7 0.6 0.6 0.5 0.3 0.2 0.1 0.1 0.1 0.0 0.0

China India Indonesia Japan Bangladesh Pakistan Vietnam Philippines Thailand Iran Korea Myanmar ROC Sri Lanka Nepal Malaysia Cambodia Hong Kong Lao PDR Singapore Mongolia Fiji Bhutan

987.1 697.2 147.5 117.1 85.4 82.6 53.7 48.1 44.8 38.8 38.1 31.8 17.9 14.7 14.6 13.9 6.59 5.06 3.20 2.41 1.66 0.63 0.41

40.0 28.3 6.0 4.7 3.5 3.3 2.2 1.9 1.8 1.6 1.5 1.3 0.7 0.6 0.6 0.6 0.3 0.2 0.1 0.1 0.1 0.0 0.0

Bahrain Kuwait Oman Qatar Saudi Arabia UAE Brunei (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLVM GCC (reference) US EU15 EU28 Australia Turkey

0.21 0.74 0.68 0.11 5.84 0.25 0.13

0.0 0.0 0.0 0.0 0.3 0.0 0.0


0.34 1.36 1.09 0.22 9.91 1.04 0.19

0.0 0.1 0.0 0.0 0.4 0.0 0.0

1147.5 57.0 2005.1 99.6 2012.9 100.0 986.8 49.0 709.8 35.3 279.5 13.9 200.3 9.9 79.3 3.9 7.82 0.4 205.1 342.1 439.9 12.6 35.6

10.2 17.0 21.9 0.6 1.8

1434.0 58.1 2453.5 99.4 2467.4 100.0 1166.8 47.3 895.0 36.3 352.2 14.3 256.9 10.4 95.3 3.9 14.0 0.6 227.2 357.3 461.8 14.7 44.7

9.2 14.5 18.7 0.6 1.8

1990

(%)

1143.3 China 870.6 India 179.4 Indonesia 123.6 Japan 112.1 Pakistan Bangladesh 109.0 Vietnam 66.0 60.7 Philippines 55.1 Iran 54.5 Thailand 42.9 Korea 40.2 Myanmar 20.4 ROC 18.1 Malaysia 18.1 Nepal Sri Lanka 17.0 8.84 Cambodia 5.70 Hong Kong 4.14 Lao PDR 3.05 Singapore 2.07 Mongolia 0.74 Fiji 0.54 Bhutan

38.4 29.2 6.0 4.1 3.8 3.7 2.2 2.0 1.8 1.8 1.4 1.3 0.7 0.6 0.6 0.6 0.3 0.2 0.1 0.1 0.1 0.0 0.0

0.49 2.10 1.63 0.42 16.4 1.77 0.25

0.0 0.1 0.1 0.0 0.5 0.1 0.0


1772.0 59.5 2956.3 99.2 2979.0 100.0 1338.0 44.9 1127.3 37.8 435.2 14.6 316.0 10.6 119.2 4.0 22.8 0.8 249.6 366.3 475.2 17.1 56.5

8.4 12.3 16.0 0.6 1.9

2000

36.9 30.7 6.0 4.0 3.7 3.6 2.3 2.2 1.9 1.8 1.4 1.3 0.7 0.7 0.6 0.6 0.3 0.2 0.2 0.1 0.1 0.0 0.0

0.64 1.86 2.40 0.61 21.4 3.00 0.32

0.0 0.1 0.1 0.0 0.6 0.1 0.0


2093.3 60.9 3407.2 99.1 3437.1 100.0 1472.7 42.8 1357.9 39.5 511.6 14.9 371.2 10.8 140.3 4.1 29.9 0.9 282.2 377.6 487.3 19.0 67.8

2010

(%)

1267.4 China 1053.5 India 206.3 Indonesia 137.9 Pakistan 126.9 Japan Bangladesh 124.1 Vietnam 77.6 76.5 Philippines 64.2 Iran 60.6 Thailand 47.0 Korea 45.6 Myanmar 23.5 Malaysia 22.8 Nepal 22.3 ROC Sri Lanka 19.1 11.9 Cambodia 6.67 Hong Kong 5.22 Lao PDR 4.03 Singapore 2.39 Mongolia 0.80 Fiji 0.60 Bhutan

8.2 11.0 14.2 0.6 2.0

(%)

1340.9 China 1231.0 India 237.6 Indonesia 173.5 Pakistan Bangladesh 147.3 128.1 Japan Philippines 92.3 86.9 Vietnam 74.3 Iran 65.9 Thailand 49.7 Myanmar 49.6 Korea 28.6 Malaysia 26.4 Nepal 23.2 ROC Sri Lanka 20.7 14.0 Cambodia 7.02 Hong Kong 6.26 Lao PDR 5.08 Singapore 2.76 Mongolia 0.86 Fiji 0.70 Bhutan

34.8 31.9 6.2 4.5 3.8 3.3 2.4 2.3 1.9 1.7 1.3 1.3 0.7 0.7 0.6 0.5 0.4 0.2 0.2 0.1 0.1 0.0 0.0

1.23 2.91 2.77 1.70 28.1 8.26 0.39

0.0 0.1 0.1 0.0 0.7 0.2 0.0


2421.3 62.8 3813.0 98.8 3858.0 100.0 1551.5 40.2 1599.5 41.5 586.8 15.2 430.0 11.1 156.9 4.1 45.0 1.2 309.3 397.3 503.2 22.0 73.7

8.0 10.3 13.0 0.6 1.9

2015

(%)

1374.6 China 1311.1 India 253.3 Indonesia 192.7 Pakistan Bangladesh 158.0 127.1 Japan Philippines 101.0 91.7 Vietnam 79.1 Iran 67.2 Thailand 51.9 Myanmar 51.0 Korea 31.0 Malaysia 27.8 Nepal 23.5 ROC Sri Lanka 21.0 15.2 Cambodia 7.31 Hong Kong 6.85 Lao PDR 5.54 Singapore 3.01 Mongolia 0.89 Fiji 0.76 Bhutan

33.9 32.3 6.2 4.8 3.9 3.1 2.5 2.3 2.0 1.7 1.3 1.3 0.8 0.7 0.6 0.5 0.4 0.2 0.2 0.1 0.1 0.0 0.0

1.37 3.54 4.43 2.15 31.5 9.09 0.42

0.0 0.1 0.1 0.1 0.8 0.2 0.0


2574.1 63.5 4001.7 98.7 4053.9 100.0 1586.6 39.1 1711.2 42.2 624.0 15.4 458.4 11.3 165.6 4.1 52.1 1.3 320.9 404.6 508.4 23.8 78.7

7.9 10.0 12.5 0.6 1.9


Unit: Millions of persons. Sources: Population census and other official data in each country, including author interpolations.

when Hong Kong was returned to China, and subsequently plummeted until 2004. Singapore followed a similar path to that of Hong Kong – peaking in 1996, and falling to an all-time low in 2002 before the surge from the late 2000s. The ROC and Korea moved together but at a lower level than Singapore and Hong Kong. The views found in Table 5 are considerably revised if focusing on production or real income per capita, using PPP as the conversion rates. In terms of per capita GDP at constant prices using PPP in Table 6, Japan was the first country in Asia to start catching up with the US. By 1970, its per capita GDP was

26


Table 5 Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015

_GDP at current market prices per person, using annual average exchange rate 1970

1980

(%)

(%)

1990

(%)

2000

(%)

2010

(%)

2015

(%)

Japan Hong Kong Singapore Fiji Iran ROC Malaysia Korea Bhutan Sri Lanka Thailand Philippines Pakistan Bangladesh Cambodia India China Myanmar Nepal Mongolia Indonesia Lao PDR Vietnam

1.99 100.0 0.96 48.4 0.93 46.5 0.43 21.5 0.40 19.9 0.39 19.7 0.36 17.9 0.28 14.0 0.23 11.5 0.23 11.4 0.21 10.7 0.18 9.3 0.17 8.4 0.14 7.0 0.12 6.0 0.11 5.8 0.11 5.6 0.10 5.0 0.10 5.0 0.09 4.7 0.09 4.3 0.07 3.3 0.03 1.4

Japan Hong Kong Singapore Iran ROC Fiji Malaysia Korea Thailand Philippines Indonesia Bhutan Sri Lanka China Pakistan Mongolia India Bangladesh Myanmar Nepal Lao PDR Cambodia Vietnam

9.29 100.0 5.70 61.4 5.00 53.9 2.51 27.0 2.37 25.5 1.92 20.7 1.78 19.1 1.70 18.4 0.74 8.0 0.69 7.4 0.54 5.8 0.34 3.6 0.33 3.6 0.31 3.3 0.29 3.1 0.29 3.1 0.27 2.9 0.22 2.4 0.19 2.0 0.18 1.9 0.14 1.6 0.11 1.2 0.02 0.2

Japan Hong Kong Singapore ROC Korea Malaysia Fiji Iran Thailand Philippines Mongolia Indonesia Bhutan Sri Lanka Pakistan India China Lao PDR Bangladesh Nepal Cambodia Myanmar Vietnam

25.3 100.0 13.5 53.3 12.8 50.4 8.17 32.3 6.52 25.7 2.50 9.9 1.86 7.3 1.72 6.8 1.63 6.4 0.77 3.0 0.77 3.0 0.71 2.8 0.58 2.3 0.55 2.2 0.39 1.5 0.38 1.5 0.35 1.4 0.30 1.2 0.29 1.1 0.25 1.0 0.20 0.8 0.14 0.6 0.10 0.4

Japan Hong Kong Singapore ROC Korea Malaysia Fiji Thailand Iran Philippines Sri Lanka China Indonesia Bhutan Mongolia Pakistan India Vietnam Bangladesh Lao PDR Cambodia Nepal Myanmar

38.5 100.0 25.8 66.9 23.8 61.8 14.9 38.6 11.9 31.0 4.04 10.5 2.11 5.5 2.09 5.4 1.72 4.5 1.06 2.8 1.01 2.6 0.96 2.5 0.82 2.1 0.74 1.9 0.60 1.6 0.52 1.4 0.46 1.2 0.42 1.1 0.42 1.1 0.32 0.8 0.31 0.8 0.28 0.7 0.17 0.4

Singapore Japan Hong Kong Korea ROC Malaysia Iran Thailand China Fiji Indonesia Sri Lanka Mongolia Bhutan Philippines India Vietnam Lao PDR Pakistan Cambodia Bangladesh Myanmar Nepal

46.6 100.0 44.5 95.6 32.6 69.9 22.1 47.4 19.3 41.4 8.92 19.2 6.42 13.8 5.19 11.1 4.55 9.8 3.68 7.9 3.18 6.8 2.72 5.8 2.61 5.6 2.28 4.9 2.16 4.6 1.36 2.9 1.35 2.9 1.10 2.4 1.01 2.2 0.81 1.7 0.78 1.7 0.75 1.6 0.72 1.5

Singapore Hong Kong Japan Korea ROC Malaysia China Thailand Iran Fiji Mongolia Sri Lanka Indonesia Philippines Bhutan Vietnam Lao PDR India Pakistan Bangladesh Cambodia Myanmar Nepal

53.6 100.0 42.4 79.0 34.5 64.3 27.1 50.5 22.4 41.7 9.56 17.8 8.01 14.9 6.00 11.2 5.00 9.3 4.97 9.3 3.92 7.3 3.83 7.1 3.40 6.3 2.90 5.4 2.74 5.1 2.13 4.0 1.87 3.5 1.61 3.0 1.39 2.6 1.23 2.3 1.21 2.3 0.93 1.7 0.79 1.5

Bahrain Kuwait Oman Qatar Saudi Arabia UAE Brunei (regrouped) APO20 Asia24 Asia30 East Asia South Asia ASEAN ASEAN6 CLVM GCC (reference) US EU15

1.88 94.7 4.00 201.2 0.40 19.9 4.97 250.0 0.92 46.4 4.28 215.4 1.43 71.9

10.3 21.8 5.79 35.4 16.7 42.3 26.7

110.9 234.9 62.4 381.4 179.5 455.3 287.9

34.2 53.5 21.3 76.7 23.1 91.8 46.1 9.1 6.5 6.7 12.6 1.2 3.1 4.0 0.9 33.1

3.58 179.9 0.68 34.4

Australia Turkey

11.8 127.1 2.06 22.2

Australia Turkey

36.4 26.4 22.8 21.5 4.03

94.7 68.7 59.2 55.9 10.5


22.7 42.4 33.2 61.9 16.1 29.9 78.7 146.7 20.9 39.0 42.2 78.8 30.9 57.6

3.49 2.50 2.59 4.87 0.46 1.21 1.54 0.33 12.7


20.8 44.7 40.7 87.4 21.5 46.1 75.2 161.6 18.9 40.6 35.6 76.4 35.5 76.1

Australia Turkey


13.2 20.6 8.22 29.5 8.89 35.3 17.8

12.6 135.7 9.29 100.1


9.25 36.5 9.10 35.9 7.21 28.5 17.8 70.4 7.19 28.4 28.9 114.4 13.4 53.1

5.25 263.9 3.65 183.4


0.31 0.23 0.23 0.32 0.12 0.12 0.15 0.06 1.36

15.7 11.4 11.6 16.3 6.2 6.3 7.5 3.1 68.2

1.22 13.1 0.84 9.1 0.94 10.1 1.31 14.1 0.27 2.9 0.56 6.0 0.73 7.9 0.09 0.9 18.4 197.9

2.56 1.67 1.73 3.02 0.38 0.84 1.11 0.13 9.30

10.1 6.6 6.8 12.0 1.5 3.3 4.4 0.5 36.8

24.0 17.4

94.7 68.9

19.0 3.61

75.1 14.3

4.92 4.74 4.98 8.75 1.27 3.37 4.19 1.10 25.7

10.6 10.2 10.7 18.8 2.7 7.2 9.0 2.4 55.2

48.4 103.9 36.8 79.0 33.4 71.7 58.7 126.1 10.5 22.5

4.69 5.78 6.07 11.1 1.56 3.91 4.72 1.66 27.5

3

8.7 10.8 11.3 20.7 2.9 7.3 8.8 3.1 51.2

56.2 104.8 41.1 76.6 37.9 70.7 52.3 97.5 10.9 20.4

61% of the US, quite a distance ahead of other Asian countries. Japan had been closing the gap with the US steadily until 1991 (86%), but the gap widened again when the impact of the long recession of the 1990s started to manifest itself.17 In recent years, Japan’s level has stabilized to around 70–73% of the US, as shown in Figure 13.

27


Unit: Thousands of US dollars. Sources: Official national accounts in each country, including author adjustments. Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

US=100 in each year

US=100 in each year

160

120

140 Japan 120 100

100

US Singapore

80

US

60

Hong Kong

80 40

60 Australia 40 20 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

20

ROC

Korea

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 11 Per Capita GDP using Exchange Rate of Japan and Australia, Relative to the US, 1970–2015

Figure 12 Per Capita GDP using Exchange Rate of the Asian Tigers, Relative to the US, 1970–2015



_GDP at current market prices per person, using annual average exchange rate, relative to the US

_GDP at current market prices per person, using annual average exchange rate, relative to the US

Japan’s per capita GDP was the highest among Asian countries until it was overtaken by Singapore in 1980.18 The result highlights the outcome of the dramatic development effort made by the Asian Tigers, as shown in Figure 14. Not only were they inching to the top, they were constantly closing the gap with the US. Starting from a level of 42% the US in 1970, Singapore surpassed the US in 1993.19 In 2015, Singapore had a per capita GDP which was 53% above the US. It became the richest economy in Asia, representing a remarkable achievement. Hong Kong holds the second place, with a per capita GDP similar to the US. Japan’s per capita GDP, at 72% of the US, or around 47% of the group leader (Singapore), is similar to that of the EU15. The ROC and Korea trail behind the other two Asian Tigers at 83% and 65% of the US, respectively.


The relative performance of China and India, the two most populous countries in the world, is diminished in this measure due to their population. Their per capita GDP is 25.7% and 10.7% of the US in 2015, respectively, as shown in Figure 15. However, this should not taint the remarkable progress made over the past decades, especially by China where the per capita GDP was less than 2.0% of the US in 1970. China’s relative per capita GDP has increased more than tenfold in these four decades. The income gap between the US and the majority of Asian countries is still sizable,20 indicating significant opportunity for catch-up.

17: Jorgenson, Nomura, and Samuels (2016) indicated that the manufacturing sector was the main contributor to the catching-up process of the Japanese economy in the 1960s, and that, by 1980, the US–Japan TFP gap for the manufacturing sector had almost disappeared. Japanese manufacturing productivity relative to the U.S. peaked at 103.8 in 1991 and deteriorated afterward, leaving a current gap that is almost negligible. 18: Among the mature economies in Asia, Singapore is a unique country, in which the PPP was downwardly revised from the 2005 ICP to the 2011 ICP (see Box 1). This shift has the significant effect of bringing forward the year when Singapore overtook Japan (or US) in relative per capita GDP to 1980 (1993 for the US), from 1993 (2004 for the US) as estimated in the Databook 2013, based on the 2005 ICP. Although this edition follows the 2011 ICP results, it may require a further examination if this time-series level comparison, based on the constant PPP approach, can provide an appropriate picture, especially for Singapore. 19: Generally, Singapore’s GNI is lower than its GDP, and over the past four decades, the divergence was the largest in 2004 with GNI equivalent to 93.0% of GDP (see Figure 90 in Section 7.1, p. 121). On the other hand, the US GNI never goes outside +1.6% of GDP. However, Singapore’s lead of 53% over the US in 2015 was large enough that their relative positions would be independent of whether GNI or GDP was used. Based on the comparison among cities in Box 8 (p. 93), the per capita GDP in Singapore was 11% above New York City and 22% above Tokyo in 2015.

28


Table 6 Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2015

_GDP at constant market prices per person, using 2011 PPP, reference year 2015 1970 Japan Singapore Iran Hong Kong Fiji Malaysia ROC Philippines Mongolia Thailand Korea Sri Lanka Indonesia Pakistan India Myanmar Bhutan Bangladesh Nepal Lao PDR Vietnam China

(%)

15.4 100.0 10.7 69.5 10.3 66.7 9.12 59.1 4.51 29.2 4.25 27.5 3.85 24.9 3.14 20.3 2.84 18.4 2.83 18.4 2.66 17.2 2.29 14.8 1.82 11.8 1.54 10.0 1.31 8.5 1.28 8.3 1.22 7.9 1.20 7.8 1.14 7.4 1.14 7.4 1.00 6.5 0.50 3.3

Bahrain 37.8 244.6 202.1 1309.1 Kuwait 15.3 99.3 Oman 168.1 1089.2 Qatar Saudi Arabia 50.2 324.9 43.8 283.8 UAE 94.8 614.1 Brunei (regrouped) APO20 3.12 20.2 Asia24 2.02 13.1 Asia30 2.19 14.2 East Asia 2.24 14.5 South Asia 1.33 8.6 ASEAN 2.11 13.6 2.52 16.3 ASEAN6 1.07 6.9 CLVM GCC 62.2 403.2 (reference) US 25.3 164.1 EU15 18.8 121.6 Australia Turkey

23.2 150.5 7.21 46.7

1980 Singapore Japan Hong Kong Iran ROC Malaysia Fiji Korea Philippines Thailand Mongolia Indonesia Sri Lanka Pakistan Myanmar India Bhutan Lao PDR Nepal Bangladesh Vietnam China

(%)

21.9 100.0 21.8 99.2 16.9 76.9 10.5 47.8 8.58 39.1 7.40 33.7 5.90 26.9 5.41 24.7 4.27 19.5 4.16 18.9 3.87 17.7 3.20 14.6 2.94 13.4 1.81 8.3 1.65 7.5 1.40 6.4 1.29 5.9 1.27 5.8 1.18 5.4 1.09 5.0 1.04 4.7 0.77 3.5

Bahrain 48.0 218.6 Kuwait 88.9 405.0 Oman 26.8 122.3 Qatar 142.8 651.1 Saudi Arabia 78.1 355.8 200.7 914.8 UAE 158.9 724.4 Brunei (regrouped) APO20 4.03 18.4 Asia24 2.70 12.3 Asia30 3.13 14.3 East Asia 3.22 14.7 South Asia 1.43 6.5 ASEAN 3.31 15.1 4.08 18.6 ASEAN6 1.24 5.6 CLVM GCC 84.6 385.6 (reference) US 31.2 142.3 EU15 24.6 112.0 Australia Turkey

26.7 121.6 8.54 38.9

1990 Singapore Japan Hong Kong ROC Korea Malaysia Iran Thailand Fiji Mongolia Indonesia Philippines Sri Lanka Pakistan Bhutan India Lao PDR China Nepal Myanmar Vietnam Bangladesh Cambodia

(%)

36.6 100.0 32.5 88.9 28.7 78.6 16.6 45.3 12.4 33.9 10.3 28.2 9.59 26.2 7.28 19.9 6.34 17.4 5.23 14.3 4.83 13.2 4.13 11.3 3.86 10.5 2.69 7.3 2.55 7.0 1.93 5.3 1.75 4.8 1.62 4.4 1.50 4.1 1.48 4.0 1.44 3.9 1.27 3.5 1.02 2.8

38.3 104.9 Bahrain 43.9 120.0 Kuwait 40.1 109.6 Oman Qatar 88.1 240.8 Saudi Arabia 44.5 121.7 120.7 330.1 UAE 89.5 244.9 Brunei (regrouped) APO20 5.49 15.0 Asia24 3.94 10.8 Asia30 4.30 11.8 East Asia 5.17 14.1 South Asia 1.96 5.4 ASEAN 4.61 12.6 5.80 15.9 ASEAN6 1.43 3.9 CLVM GCC 50.7 138.7 (reference) US 39.5 107.9 EU15 30.6 83.8 Australia Turkey

30.9 11.2

84.6 30.8

2000 Singapore Hong Kong Japan ROC Korea Malaysia Iran Thailand Fiji Indonesia Sri Lanka Mongolia Philippines China Bhutan Pakistan India Vietnam Lao PDR Myanmar Nepal Bangladesh Cambodia

(%)

55.0 100.0 36.2 65.8 36.0 65.5 29.0 52.7 22.1 40.1 16.2 29.4 12.2 22.1 10.2 18.5 7.38 13.4 6.34 11.5 5.73 10.4 4.95 9.0 4.58 8.3 3.93 7.1 3.64 6.6 3.51 6.4 2.71 4.9 2.65 4.8 2.50 4.5 2.25 4.1 1.93 3.5 1.85 3.4 1.51 2.8

47.6 86.5 Bahrain 86.9 157.8 Kuwait 43.5 79.0 Oman 115.5 209.8 Qatar Saudi Arabia 44.6 81.0 117.3 213.0 UAE 86.9 157.9 Brunei (regrouped) APO20 6.75 12.3 Asia24 5.65 10.3 Asia30 6.08 11.1 East Asia 7.81 14.2 South Asia 2.74 5.0 ASEAN 6.35 11.5 7.83 14.2 ASEAN6 2.42 4.4 CLVM GCC 55.9 101.6 (reference) US 49.0 88.9 EU15 37.2 67.6 EU28 32.7 59.5 Australia 39.3 71.5 Turkey 13.4 24.4

2010 Singapore Hong Kong ROC Japan Korea Malaysia Iran Thailand China Indonesia Sri Lanka Mongolia Fiji Bhutan Philippines Vietnam India Pakistan Lao PDR Myanmar Cambodia Bangladesh Nepal

(%)

76.8 100.0 51.2 66.7 41.9 54.6 38.1 49.6 32.3 42.1 22.1 28.8 19.0 24.7 14.7 19.1 10.1 13.2 9.17 11.9 8.81 11.5 8.09 10.5 7.88 10.3 7.15 9.3 6.05 7.9 4.82 6.3 4.75 6.2 4.50 5.9 4.20 5.5 3.94 5.1 2.78 3.6 2.69 3.5 2.43 3.2

43.4 56.6 Bahrain 84.4 109.9 Kuwait 52.6 68.5 Oman 142.5 185.6 Qatar Saudi Arabia 47.3 61.7 63.1 82.2 UAE 83.6 108.9 Brunei (regrouped) APO20 8.97 11.7 Asia24 9.32 12.1 Asia30 9.87 12.9 East Asia 13.79 18.0 South Asia 4.55 5.9 ASEAN 9.27 12.1 11.07 14.4 ASEAN6 4.34 5.6 CLVM GCC 56.4 73.5 (reference) US 52.6 68.5 EU15 40.0 52.1 EU28 36.4 47.4 Australia 46.0 60.0 Turkey 18.3 23.8

2015 Singapore Hong Kong ROC Japan Korea Malaysia Iran Thailand China Mongolia Sri Lanka Indonesia Fiji Bhutan Philippines Vietnam India Lao PDR Pakistan Cambodia Myanmar Bangladesh Nepal

(%)

86.1 100.0 56.9 66.1 46.9 54.5 40.3 46.8 36.4 42.3 26.4 30.6 17.3 20.1 16.7 19.4 14.4 16.7 12.1 14.1 11.7 13.6 11.3 13.1 9.09 10.5 8.63 10.0 7.37 8.6 6.11 7.1 6.04 7.0 5.85 6.8 4.95 5.7 3.63 4.2 3.47 4.0 3.40 4.0 2.76 3.2

3

46.8 54.4 Bahrain 83.7 97.1 Kuwait 40.1 46.6 Oman Qatar 150.3 174.5 Saudi Arabia 54.4 63.2 72.7 84.4 UAE 77.1 89.6 Brunei (regrouped) APO20 10.2 11.9 Asia24 11.6 13.4 Asia30 12.2 14.2 East Asia 17.9 20.8 South Asia 5.69 6.6 ASEAN 11.0 12.8 13.2 15.3 ASEAN6 5.05 5.9 CLVM GCC 62.1 72.1 (reference) US 56.2 65.3 EU15 41.2 47.8 EU28 37.7 43.8 Australia 48.9 56.8 Turkey 24.1 28.0

Table 6 also presents individual figures for seven oil-rich economies (Brunei and the six GCC countries). At first glance, figures in 1970, and those to a lesser extent in 1990, suggest these economies had remarkably higher per capita GDP than those of Japan and the US. For example, in 1970, Kuwait, 20: Per capita GDP may have underestimated the welfare of people in some countries. In the ROC, Hong Kong, and Japan, for example, GNI is consistently higher than GDP although the fluctuations are within +6%. The Philippines is the exception where the divergence between GNI and GDP has been increasing and has become significant for the past two decades, and GNI was more than 30% higher than GDP in the 2010s (See Figure 90 in Section 7.1).

29


Unit: Thousands of US dollars (as of 2015) Sources: Official national accounts in each country, including author adjustments. Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

US=100 in each year

100

US

US=100 in each year

160 140

90 80

Australia EU15

Singapore

120 100

US Hong Kong

80 70 60

60 Japan

40

ROC

20 50 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 13 Per Capita GDP of Japan, the EU, and Australia, Relative to the US, 1970–2015

_GDP at current market prices per person, using 2011 PPP, relative to the US Sources: Official national accounts in each country, including author adjustments.

Qatar, and Brunei had a per capita GDP 13.1 times, 10.9 times, and 6.1 times that of Japan, respectively. However, the measurement of GDP as an indicator of production is misleading for these countries, as it erroneously includes proceeds from the liquidation of a natural resource stock as part of the income flow. In other words, GDP overestimates income from the oilexporting economies because it does not account for depletion of their natural resource assets. To give a rough indication of the extent of distortion, Figure 16 provides comparisons of per capita GDP excluding production of the mining sector (e.g., crude oil and natural gas). The non-mining GDP per person in GCC economies, such as the UAE, Bahrain, and Kuwait, is almost similar to Japan’s level, although total GDP per capita is much larger.

Korea 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 14 Per Capita GDP of the Asian Tigers, Relative to the US, 1970–2015

_Ratio of per capita GDP at constant market prices, using 2011 PPP, relative to the US


US=100 in each year

25 20 15 10 5

ASEAN

India

China 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 15 Per Capita GDP of China, India, and ASEAN, Relative to the US, 1970–2015

_Ratio of per capita GDP at constant market prices, using 2011 PPP, relative to the US



Catching up with the per capita GDP level of advanced economies is a long-term process that could take several decades to accomplish. Empirical evidence suggests there may be a negative correlation between per capita GDP level and the speed of catching up, with some exceptions. With the possibility of adopting successful practices and technologies from the more advanced economies, less advanced economies are poised to experience faster growth in per capita GDP, enabling themselves to catch up to average income levels. However, as their income levels approach those of the more advanced countries, their economic growth rates are expected to gradually decline over time.21

21: The OECD (2017b) observes that GDP per capita has broadly converged in the OECD countries since the 1970s. However, more advanced economies that started with high income levels in the 1970s have had lower rates of catch-up, stagnated or recently diverged vis-à-vis the US.

30


Figure 17 plots countries’ initial per capita Qatar 78.1 150.3 72.2 Brunei 42.9 34.3 77.1 GDP levels against their respective average UAE 31.1 72.7 41.6 growth rates per year between 1970 (or the Japan 38.5 1.7 40.3 initial year data first became available for the Kuwait 46.0 83.7 37.7 country in question) and 2015. If the two Bahrain 34.1 12.8 46.8 variables have a correlation coefficient of Saudi Arabia 29.3 25.1 54.4 Non-mining GDP –0.5 (i.e., a negative relationship of medium Malaysia 22.8 3.6 26.4 Mining GDP strength), the higher the initial income level Oman 21.7 18.4 40.1 becomes, the more slowly the average growth Iran 15.2 2.1 17.3 Thousands of US dollars (as of 2015) Myanmar 2.2 1.3 3.5 rate per year is expected to move. However, this is not always true. Low-income countries 0 30 60 90 120 150 like Bangladesh, Nepal, the Philippines, and Fiji have failed to catch up, while Thailand and Figure 16 Per Capita Non-Mining GDP in OilRich Countries and Japan, 2015 Malaysia could be expected to have grown _GDP at constant market prices per person, using 2011 even faster, given their initial income levels. PPP, reference year 2015 The Asian Tigers have experienced robust Sources: Official national accounts in each country, including author growth in the past four decades, but Korea adjustments. and the ROC, with their lower initial per capita GDP, have sustained higher growth rates than Singapore and Hong Kong. Relative to the Asian Tigers, China appears to be at the start of the catchup process. Mature economies like the US, the EU15, and Japan shared similar growth experiences (around 2% on average per year, in the past four decades).

3

Table 7 summarizes Figure 17 by country groups. Four levels of per capita income groups are defined: Group-L1, with per capita GDP at or above 60% of the US; Group-L2, from 20% to under 60%; GroupL3, from 8% to under 20%; and Group-L4, below 8%. Likewise, countries are also grouped according to the speed of their catch-up with the US: Group-C1, at 3% per annum or above; Group-C2, from 1%

5

%

Growth rate of per capita GDP during 1970–2015

Cambodia

Bhutan 4

8

=1970, =1990, =2015

7

Indonesia

Mongolia

3 Pakistan Bangladesh Myanmar

2

ROC

5

Singapore Malaysia

4

Hong Kong

Thailand Philippines

Nepal

Korea

6

Sri Lanka India

=1970, =1990, =2015

China

Vietnam

Lao PDR

Growth rate of per capita GDP during 1970–2015

3

Turkey

Fiji

Japan

2

EU15

1

Australia

Iran

1 Thousands of US dollars (as of 2015)

US

Thousands of US dollars (as of 2015)

0

0 0

2

4

6

8

10

12

14

Per capita GDP at constant prices

0

10

20

30

40

50

60

70

80

90

Per capita GDP at constant prices

Figure 17 Initial Level and Growth of Per Capita GDP, 1970–2015

_Level and average annual growth rate of GDP at constant market prices, using 2011 PPP, reference year 2015 Sources: Official national accounts in each country, including author adjustments. Note: The starting period for Cambodia is 1987.

31


%

3 Economic Growth

Box 2

System of National Accounts in Asia

Understanding data comparability is essential for the construction of an international database, and requires continuous effort and expert knowledge. Between December 2016 and March 2017, the APO Productivity Database project conducted the Metadata Survey 2017 on the national accounts and other statistical data required for international comparisons of productivity among the APO member economies. Broadly speaking, cross-country data inconsistency can arise from variations in one or more of the three aspects of a statistic: definitions, coverage, and methodology. The international definitions and guidelines work to standardize countries’ measurement efforts. However, country data can deviate from the international best practice and vary in terms of omissions and coverage achieved. Countries can also vary in their estimation methodology and assumptions in benchmark and/or annual revisions. This may account for part of the differences observable in the data, as well as interfere with comparisons of countries’ underlying economic performance. Most of the economic performance indicators in this report are GDP-related. The surveys therefore put much emphasis on discerning countries’ GDP compilation practices. In the Databook 2017, the 2008 SNA is used as the standard, noting how countries’ practices deviate from it. Since there are differences between the 2008 SNA and its predecessors (1993 SNA or 1968 SNA) in some concepts and coverage, it is important to know in which year the data series definitions and classification started to switch over. This allows identification in breaks in the time series. Figure B2 presents the current situation in compilations and data availability of the backward estimates based on the 1968 SNA, the 1993 SNA, and the 2008 SNA (including the future plan for introducing the 2008 SNA), based on our Metadata Survey 2017. For example,

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Bangladesh Cambodia ROC Fiji India Indonesia Iran Japan Korea Lao PDR Malaysia Mongolia Nepal Pakistan Philippines Sri Lanka Thailand Vietnam

68 1973 93 1980 2000 08 1996 2014 68 1993 93 1993 2009 08 2019 68 1951 1988 93 1951 2005 08 1951 2014 68 1968 1974 93 1995 2003 08 2005 2008 68 1950 1978 93 1999 2007 08 2004 2010 68 1960 1970 93 2000 08 2010 2015 68 1959 1981 93 1991 2006 68 1955 1978 93 1980 2000 08 1994 2016 68 1953 1986 93 1970 2004 08 1953 2014 68 1990 93 2002 2005 68 1975 93 2000 2007 1960(mixture of 1953 SNA until 1968) 08 2005 2012 68 N.A.(Before 1993 SNA is introduced, Material Product System was used.) 93 1980 1995 08 2010 2015 68 1975 93 2000 2006 68 1981 1988 93 2000 2004 08 2006 2013 68 1946 1972 93 1998 2011 08 1998 2011 68 1975 93 1998 2001 08 2010 2016 68 1972 1975 93 1990 2012 08 2018 68 1989 93 1986 1993 08 2018


Introduction year

Backward estimates and implementation

Figure B2 Implementation of the 1968, 1993, and 2008 SNA Source: APO Metadata Survey 2017. continued on next page >

32



Japan started to publish national accounts based on the 1968 SNA in 1978 (at present, backward estimates based on the 1968 SNA are available from 1955), national accounts based on the 1993 SNA in 2000 (backward estimates based on the 1993 SNA are available from 1980 at present), and national accounts based on the 2008 SNA in 2016 (backward estimates based on the 2008 SNA are available from 1994 at present). As Figure B2 suggests, countries differ in their year of introduction, the extent of implementation, and backward estimates available. According to the survey response and our investigation, 15 countries of Asia24 are currently 2008 SNA compliant (partially or fully). While there are movements toward upgrading the SNA, Cambodia, the Lao PDR, and Nepal have yet to fully introduce the 1993 SNA. The starting year of the official 2008 or 1993 SNA compliant time series varies a great deal across countries, reflecting the differences in the availability of backward estimates. Countries may have adopted the 2008/1993 SNA as the framework for their national accounts, but the extent of compliance in terms of coverage may also vary. The APO Productivity Database tries to reconcile the national accounts variations based on the metadata information and our investigation, and provide harmonized estimates for international comparison. See Appendix 1 for details of the adjustments.

to under 3%; Group-C3, from 0% to under 1%; and Group-C4, under 0%. The speed of catch-up with the US is defined as the difference in the average annual growth rate of per capita real GDP between each country and the US. Table 7 shows that many Asian countries (not belonging to Group-C4) have managed to close the gap in per capita real GDP with the US over the last four decades, although some are more successful than others.

3

Table 7 Country Groups Based on the Initial Economic Level and the Pace of Catching Up _Level and average annual growth rate of per capita GDP at constant market prices, using 2011 PPP Initial GDP level to the US

Annual rate of catch-up to the US (C1) > 3%

(C2) 1% Japan (continued from previous page) c) Environment-energy

- Promotion of investment of distribution and service industries and small and medium-sized companies in energy efficiency - Promotion of introduction of renewable energy and development of new energy systems - Strengthening of resource security

d) Sports/Culture

- Improvement the attractiveness and profitability of sports facilities - Development and use of sports management human resources and creation of a platform - Integration of sports with IT, healthcare, tourism, fashion, culture and arts, etc. and its expansion - Establishment of an existing housing transaction market in which houses are evaluated as assets

e) Service

- Creation of businesses leading the service industry's productivity improvement - Productivity improvement by each business field - Community-based productivity improvement through utilization of SME support providers, etc.

f ) SME

- Support for the growth of local core companies with an eye to the global market - Support for local SME's expansion to overseas by taking advantage of TPP - Support for improvement of productivity of SMEs including IT utilization - Promotion of provision of growth funds which do not need collateral or survey by using a “local benchmark“, etc., enhancement of financing functions and acceleration of business revitalization/succession

g) Agriculture

- Enhancement of functions of the Public Corporations for Farmland Consolidation to Core Farmers through Renting and Subleasing - Cost reduction of production materials and realization of distribution/processing structures favorable to producers - Development of human resources - Reinforcement of export - Promotion of “Smart agriculture“ (realizing unmanned automatic operation by remote monitoring by 2020) - Building a system for cooperation between the agricultural and business communities

h) Tourism

- Enhancement of the attractiveness of tourism resources - Revision of tourism-related regulations and systems - Promotion of establishing and developing Destination Management/Marketing Organization (DMOs) - Development of tourism human resources - Improvement of regional tourism environment to accept an increasing number of foreign visitors to Japan - Promotion of taking leaves/staggered holidays

Korea National Strategy Project (Ministry of Science, ICT and Future Planning, August 10, 2016) Focus Sectors of the Development Plan a) Power for new growth engine

- Developing core technology for self-driving cars - Develop lightweight materials - Establish smart city to lead world - Develop AI (Artificial Intelligece) to lead intelligent information society - Build Vitural Reality ecosystem

b) Quality improvement for life

- Technology development for precise medical treatment based on bio information - Develop bio new medicine to overcome severe disease - Technology development for carbon resourses - Technology development for super fine dust

Lao PDR Five Year National Socio-economic Development Plan VIII, 2016-2020 (Ministry of Planning and Investment, February 25, 2015) Key Numerical Targets Relevant for Productivity 1) S ustained, inclusive economic growth with economic vulnerability (EVI) reduced to levels required for LDC graduation and consolidated financial, legal and human resources to support growth.

- Sustained and inclusive economic growth - Integrated development planning and budgeting - Balanced regional and local development - Improved public /private labor force capacity - Local entrepreneurs are competitive in domestic and global markets - Regional and international cooperation and integration

2) H uman resources development achieved to LDC graduation criteria level and achievement of off-track MDGs through the provision and use of services which are balanced geographically and distributed equitably between social groups.

- Improved living standards through poverty reduction - Food security ensured and incidence of malnutrition reduced - Access to high quality education - Access to high quality health care and preventative medicine - Enhanced social welfare - Protection of traditions and culture - Political stability, order, justice, gender equality

3) R educed effects of natural shocks as - Environmental protection and sustainable natural resources management required for LDC graduation and sus- - Preparedness for natural disasters and risk mitigation tainable management of natural re- - Reduced instability of agricultural production sources exploitation.


Focus Sectors of the Development Plan a) Ensuring Sustained and Inclusive Eco- - Industrial Sector: Develop the industrial sector to be a high growth sector quantitatively and qualitatively, to dinomic Growth versify in its commercial and modern goods production to be able to meet the domestic consumption as well as for exports’ needs and transforming it into a main sector to support the sustainable economic growth. - Service Sector: Develop the service sector firmly linked to production enabling high revenue for the country, creating jobs to the people; enhance land transit points in the region and provide diversified goods and services to meet domestic and international markets demand. - Agriculture and Forest Sectors: Develop the agriculture-forest sector so to ensure stabile and sustainable production of food and commercial goods; expand agriculture production according each local area potential in the orientation of clean and modern and qualitative intensive agriculture; apply modern techniques and technology into production to ensure the agriculture goods supply to industrial processing and service sectors quantitatively and qualitatively linking it to the goods processing for value added enhancement; and sustainably manage the forest.

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8.1 National Development Strategies

> Lao PDR (continued from previous page) b) Improved Public / Private Labor force - Develop workforce in both quantity and quality of various areas at different levels according to the economic Capacity structure by focusing on agriculture, industry and services areas in respect to the national development demand and build capacity for them to compete in the region especially in the ASEAN region. Build workforce to be national conscious, be a good citizen, respect laws and regulations, highly discipline, ethical, tolerant and active to their work and self-development and thereby become healthy workforce which is significantly essential for each period of the socioeconomic development. c) L ocal Entrepreneurs are Competitive - Human development is a key factor of the national development especially in the generation of advanced sciin Domestic and International Markets ence, technology and integration. Therefore, it is essential to build human resources to be knowledgeable, competent and proficient in various areas of education and professions, build experts in specific fields to employ them in public and private organizations and thereby contribute in the national development and construction as they are capable to run private business most efficiently and effectively. At the same time, it is essential to concentrate in developing and managing technical services to build experts who specialized in each profession that can support the national development in each period. d) Food security Ensured and Incidence - Ensure food security and dietary intake of essential nutrients and safe for health which count from the production of Malnutrition Reduced to the consumption processes, aiming at contributing to poverty reduction and livelihood improvement for the people in a sustainable manner. e) Universal Access to Quality Education

- Improve and modify curriculum for vocational and university educations in the fields that suit the socio-economic development such as electrical, mining, processing, handicraft, mechanics and services. This is to help ensuring higher quality of the education and sufficient labor force to employ in production and services sectors that supporting the future national development, and enhance capacity to integrate and compete at the regional and international level.

8

f ) E nvironmental protection and sustain- - To ensure sustainable development, initiatives on environmental protection and disaster risks management are able natural resources management essential. Develop green, clean and beautiful economy for the people’s well-being, health and pollution free by sustainable urbanization. Increase capacity on climate change resilience and impact mitigation. g) Prepare to cope with the disaster risks - Further develop sustainable ownership to protect the environment, ready to cope and reduce the risk of various and climate change disasters, economic green people live a good, healthy, strong and safe from pollution of water, sound and air. Increase and improve the ability to adapt, to reduce the impact of climate change by reducing greenhouse gas emissions to a minimum. h) Reducing the instability of agricultural - To meet the sustainable development and stable and continued economic growth, reducing the instability of production caused by the impact of agriculture production is essential to foster economic growth and promoting stable jobs for farmer. In connection disasters(ensure about markets and to this, preparing for and anticipating the environment condition and climate change are the main factors for agriculture. Moreover, it is also important to ensure stable supply, markets and prices for the agricultural products. prices for the agricultural products

Malaysia 11TH MALAYSIA PLAN, 2016-2020 (Economic Planning Unit, Prime Minister's Department, May 21, 2015) Key Numerical Targets Relevant for Productivity - Labour productivity will reached USD21,000 in the year 2020 from USD17,500 in 2015 Focus Sectors of the Development Plan a) National Level

- Formulating a five-year Malaysia Productivity Blueprint - Enhancing public sector productivity by introducing productivity, enhancement KPIs, accelerating regulatory reforms and rationalizing government institutions - Encouraging up-skilling and re-skilling as well as research through increased industry-academia collaboration, more targetted training programs and increased support for industrial and scoial innovation activities

b) Industry Level

- Appointing productivity champions and customising industry-level productivity programmes

c) Entreprise Level

- Setting up enterprise-level productivity assessments and targets by promoting productivity performance targets, introducing firm level interventions, promoting health check mechanisms and fostering productivity-based culture

Mongolia Mongolia Sustainable Development Vision 2030, 2016-2030 (Phase I (2016-2020) Phase II (2021-2025) and Phase III (2026-2030) (State Great Hural of Mongolia, February 5, 2016) Key Numerical Targets Relevant for Productivity - Increase GNI per capita to USD 17,500 to become an upper middle-income country - Average economic growth rate of no less than 6.6% - Become among first 40 countries by the Doing Business Index and among 70 countries by the Global Competitiveness Index - Adopt advanced technologies with high productivtiy in each sector and encourage new products, production and services adopting innovations a) Agriculture

- Increase productivity through sustainable agribusiness to promote agriculture infrastructure and rural development - Adopt economical and efficient advanced clean agro technology - Develop intensified farming

b) Tourism Sector

- Improve business, law and economic environment for sustainable production - Improve the inftrastructure and service quality - Eco-tourism development

c) Industry Sector

- Employ advanced methods, technology and innovations to increase productivity and competitiveness - Food security - Mongol branding for international market/export - Develop the chemical industrial sector

d) Mining Sector

- Potential mineral resource exploration - Develop environment friendly infrastructure - Create a favorable environment for investment in mining sector

e) Energy and Infrastructure Sector

- Become energy export country - Increase the share of renewable energy consumption - Expand information technology and telecommunication coverage - Improve the urben planning and development - Expand and develop roads and transportation logistics - Improve trade and services; develop transportation and logistics networok for import/export of goods

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Focus Sectors of the Development Plan

8 Development Policy

> Mongolia (continued from previous page) f ) Public Sector

- Accountable and transparent governance - Transfarency in administration Ethics in public sector - Leadership of public organizations PPP

g) Sustainable Social Development

- Improve quality and access too health care services - Introduction of labor market system that values workers' productivity, focusing on human development - Ensure gender equality in social development - Support youth employment by training to provide proper knowledge and skills - Coherence of science and industry to promote and adopt innovation - Improve quality of general education system and build science technology cluster

h) Environmental Sustainability

- Resource efficiency - Support clean technology and encourage low-waste and sustainable production and consumption - Improve city planning and waste management system - Develop the green development standard

i) Governance for Sustainable Develop- - Establish and strengthen an accountable and transparent governance ment - Public Private Partnership (PPP) - Improve the leadership in public organizations

Nepal 14th National Development Plan 2073-2076, 2016-2019 (National Planning Commission (NPC), February 16, 2017) Focus Sectors of the Development Plan a) reducing absolute poverty b) sharing economic prosperity c) post-earthquake reconstruction and rehabilitation d) development of physical infrastructure e) good governance

Pakistan Pakistan2025 One Nation - One Vision, -2025 (Planning Commission, Ministry of Planning, Development, and Reform, Government of Pakistan, August 1, 2014) Key Numerical Targets Relevant for Productivity - 2014-2025: Strengthening Pakistan's development foundations to become top 25 global economy and and a upper middle income country by 2025 - Double the share of total factor productivity (from the level of 2013) by 2025 - 2025-2035: Attaining regional and global leadership in ket target sectors - 2035-2047: Putting Pakistan on a fast track of development with the ultimate goal of transforming it to become one of top ten economies in the world by 2047 (The centennial year of our independence) Focus Sectors of the Development Plan - The seven pillars of Vision 2025 are based on the imperatives of embracing change and transformation, and to create new opportunities based on the country's innate strengths. a) People First: Developing social and - Population management human capital and empowering - Basic and college education women - Health - Labour, employment and skill development - Poverty alleviation and sustainable development goals - Social welfare - Gender and women empowerment - Youth and sports - Religious pluralism and interfaith harmony - Mass media, culture and national heritage rowth: Achieving sustained, indige- - Fiscal, monetary and capital market development b) G nous and inclusive growth - Trade and commerce – Balance of payments - Balanced development – Focus on the less developed regions - Physical planning and housing - Improvement in existing industrial zones with focus on - resource efficiency, technical skills development, Marketing skills development, improve quantity and quality of raw material and supply chain c) G overnance: Democratic governance, - Institutional reforms, good governance, and modernization of the public sector institutional reform and modernization of the public sector


d) Security: Energy, water and food

- Energy: double power generation to over 45,000 MW ; development of indigenous energy resources, such as coal, hydro, alternative and renewable sources; energy efficiency. - Water: increase storage capacity, improve efficiency of usage in agriculture; construction of small and medium dams, and modernisation and improvement of existing irrigation system - Food: Reduce food insecure population from 60% to 30% and agricultural development - Nutrition: Launching policies, programs, and projects to improve the nutrition situation - Environment and climate change: institutional capacity-building to combat disasters; Strategy to develop forest cover and along with conservation and restoration of the natural resources; The COP 21agreement will be implemented to stabilize the climate and avoid its worst impacts. - Establishment of new green industrial parks for Mining and Precious Stones sector, Agro-based, exhibition center/ facilitating centers and capacity building centers in different provinces, easy and direct excess (Road) to airports and dry ports for industrial clusters - Promoting joint venture leading to clean technology transfer

e) Entrepreneurship: Private sector and - Manufacturing, commerce and mineral sectors; create at least 5 global Pakistani brands (having more than 50% entrepreneurship led growth sales coming from consumers outside Pakistan), and make ‘Made in Pakistan’ a symbol of quality - Establishment of Venture Capital Fund for Start Ups and Innovation supports. - Encourage micro businesses and promote entrepreneurship & innovation

140


> Pakistan (continued from previous page) f ) Knowledge economy: Developing a - Higher education competitive knowledge economy - Science and technology through value addition - Information and communications technology g) Connectivity: Modernizing transpor- - Road connectivity, transport, and logistics tation infrastructure and greater re- - Increase in annual exports gional connectivity

Philippines Philippine Development Plan, 2011-2016 (National Economic and Development Authority (NEDA)) Key Numerical Targets Relevant for Productivity - Improved global competitiveness to top 30% in global universe - Annual average real GDP growth increased by 7-8% - Agri, fishery & forestry output increased by 4.6-5.7%; industry by 8.1-9.1% increase; service output increased by 7.1-8%. - Generated employment for industry & service sectors by additional 4.67 million (other target) Focus Sectors of the Development Plan a) Industry and Services

8

- Business environment improved (e.g., public and business satisfaction with public services improved) - Sectors made globally competitive and innovative - Productivity increased (e.g., merchandise and service exports increased; total approved investments increased)

b) C ompetitive & Sustainable Agriculture - Food security improved & Fisheries Sectors - Incomes in agriculture & fiehsry sector increased - Sector resilience to climate change risks increased - Growth in agriculture & fishery sector increased c) G ood Governance and the Rule of law - Improved scores on the indices relating to corruption and ruling (Effective & transparent governance - Established and operationalized Integrity Infrastructure Development and Centralised Case Monitoring System practices, Enhanced access to justice) - Improved government accountability, transparency and efficiency - Increased constructive engagement between CSO/private groups and government d) Infrastruture Development

- Performance of tourism, agriculture and industries improved - Access to goods and services improved (education, health, housing, etc.) - Environmental quality improved - Resilience to climate change and natural disasters increased

e) Social Development (Health, Nutrition - Improved access to quality health and nutrition services and Population management, Educa- - Improved access to quality education, training and culture tion, Training and Culture) - Improved access to asset reform f) C onservation, Protection & Rehabilita- - Natural resources conserved, protected and rehabilitated tion of the Environment and Natural - Environmental quality for a cleaner and healthier environment improved Resources - Waste generated and waste disposal improved - Resilience of natural systems enhanced with improved adaptive capacities of human communities - Water poullution reduced g) Peace and Secutiry

- All armed conflicts brought to a permanent and peaceful closure - Safer and more secured environment conducive to national development created and sustained

Singapore 1. Plans from the Future Economy Council , 2. Research, Innovation & Enterprise 2020 Plan(RIE2020), 3. Singapore Budget 2016, (1. Future Economy Council, May 2017, 2. National Research Foundation, January 2016, 3. Ministry of Trade and Industry, March 2016) Key Numerical Targets Relevant for Productivity - From 2009 to 2020, target productivity growth is 2% to 3% per annum Focus Sectors of the Development Plan a) Develop skills for the future and sup- - Advancing SkillsFuture: Developing an integrated system of education, training, and career progression for all port productivity-led economic growth Singaporeans - Driving industry transformation: Overseeing implementation of plans for key clusters through skills development, innovation, productivity and internationalisation strategies - Fostering a culture of innovation and lifelong learning in Singapore b) A dvanced Manufacturing and Engi- - Eight key industry verticals based on the potential of Singapore to achieve global leadership, the presence of new neering Vision: Innovative, Competi- opportunities for growth and the ability to generate good jobs for Singaporeans are: Aerospace, Electronics, tive Economy Chemicals, Machinery & Systems, Marine & Offshore, Precision Modules & components, Biologics & Pharmaceutical Manufacturing, Medical Technology Manufacturing - Four cross-cutting technology areas have also been identified as essential enablers to support the eight key industry verticals: Robotics and Automation, Digital Manufacturing, Additive Manufacturing, Advanced Materials ealth & Biomedical Sciences Vision: - Healthcare is a huge and rapidly growing global market, with the biomedical sector remaining an important c) H Biomedical Innovations, Better Health- contributor to Singapore manufacturing economy. Singapore also aims to develop healthcare services, drugs or care devices that will deliver better health outcomes for Singaporeas and enable a sustainable healthcare system.

e) Services & Digital Economy Vision: - Use digital innovation as a force multiplier to meet national priorities and enhance productivity in services sector Connected City, Smart Nation - Three critical focus areas are: (E1) Urban Mobility: fusion of traditional transport engineering with autonomous technologies, real-time analytics, modelling and simulation to manage real-time traffic events; (E2) Healthcare ICT: predictive analytics and machine learning based on real-time data collected from IoT healthcare devices to meet demands of ageing society; (E3) Services Productivity: automation of knowledge work, discovery of insight through data mining and creation of iinovative digital applications to improve delivery of government and private sector services. f ) Transforming Enterprises

- Provide grants on capability building, training and internatl expansion - Support automation to scale up - Provide financing and tax incentives to support scale-ups e.g. through M&A - Support internationalization of SMEs

141


d) Urban Solutions & Sustainability Vi- - Enhance living environment to address resource constraints through an interdisciplinary approach including sion: Green City, Best Home devising new urban mobility solutions, optimizing liveable space, building the next generation smart grid, lowering energy consumption of used water treatment and seawater desalination.


> Singapore (continued from previous page) g) Transforming Industries

- National Trade Platform through internet - Leveraging new technologies to support robotic deployment across sectors such as Healthcare, Construction, Manufacturing and Logistics - Increasing outreach to SMEs through trade associations, chamber of commerces, etc

h) Transforming through Innovation

- Deepening innovation capabilities through industry-research collaboration - SG-Innovate: promote start-ups and entrepreneurship - Jurong Innovation District: create open innovate urban environment to bring together elements for the future society

i) Supporting change

Singaporeans

through - Adapt and Grow: help people adapt to changing job demands and grow their skills - TechSkills Accelerator: help people learn new ICT skills quickly though skills development and job placements

Sri Lanka National Development Plan, - 2020 (Sri Lanka Country paper of WSM and various web sites, October 19-21, 2016) Key Numerical Targets Relevant for Productivity - Enhancing productivity of major sectors contributing to the GDP by 1% annually - Placing Sri Lanka within the first 50 countries of World Innovative Index by 2020 - Elevating all the government institutions and schools to “A” Grade level of their performance by 2020 - Upgrading 3000 Small Entrepreneurs up to Medium level and 1000 Medium Entrepreneurs up to Large Scale by 2020 - Developing 3000 community productivity villages by 2020 - Productivity for all by 2020 - Generating of one million employment opportunities - Enhancing income levels - Development of rural economies - Ensuring land ownership to rural and estate sectors, the middle class and government employees - Creating a wide and a strong middle class



142

a) Agriculture

- Labour is exiting the agriculture sector: but slowly - Overall have grown and become more diverse in nature, compared to those of the plantation dominated era - Increase support for success in existing farms - Increase mobility towards more productive farming - Increase opportunities for mobility to the industrial sector

b) Tourism Sector

- To achieve 2.0 million tourist arrivals by the year 2016 - Target the upscale Free Independent Travellers (FITS), who are comparatively high spenders. - Make tourism Sri Lanka’s third largest foreign exchange earner - Transform tourism to become the fastest job creator and help reduce the unemployment levels

c) Industry Sector

- Not Available

d) Energy and Infrastructure Sector

- Development of the Electricity Master Plan through 2040 including the development of the plan for power generation, power transmission and distribution

e) Favorable Business Environment

- Not Available

f ) Sustainable Social Development

- Ambitious plan to develop the country's Western Province, which includes Colombo, into a massive urban agglomeration

g) Environmental Sustainability

- Not Available

h) Key Sectors of Investment

- Tourism and Leisure - Agriculture - Export Manufacturing - Export Services - Apparel Industry - Infrastructure - Knowledge Services - Utilities - Education

j) SME Sector

- Promoting high potential, viable and promising sectors - Focus on cluster approach for SME Development. Under the cluster approach, the entire value chain from input supply to processing and export will be supported and promoted - Promote enterprises with high value addition and encourage enterprises that use the local raw materials as they offer strong comparative advantages due to factor endowments - Motivate and encourage export oriented or import substitution enterprises and industries - Encourage and promote flagship SME sectors with high potential spillover effects to facilitate job creation or employment generation - Promotion and relocation of industries in the backward regions to reduce urban-rural imbalances - Transforming the landscape of the SMEs away from mere trade and commerce towards production and industry based with special focus on high value addition, innovative and usage of modern appropriate technology - Strengthening the SME sector in order to enhance inclusive economic development and thus provide opportunities for better employment and higher income - Achieve regionally balanced growth across Sri Lanka - Promote resource efficiency at all levels including the use of Green Technology

k) National Road Masterplan

- Increase Economic Growth to 8.5% by 2010 - Socially Inclusive Development - Economy and Efficiency in the Provision of Roads - Safety - Asset Sustainability - Sustainable institutional capacities - Environment - Private Sector Participation


Thailand National Development Plan, 2017-2021 (Office of The National Economic and Social Development Board, September 22, 2016) Key Numerical Targets Relevant for Productivity - 40% of lowest income group will get more income at least 15% - GDP increases average 5% per annum - Incomer per capita not less than USD 8,200 - Reduce green house gas from energy sector and transportation at least 7% within 2020 - Growth rate of Agricutural (3%), industrial (4.5%) and service sector (6%) Focus Sectors of the Development Plan a) Agriculture and Biomass

- Promote Sufficiency Economy for agriculture production - Achieve global safety and quality standard - Utilize Bio Base - Promote organic agri-products, production zoning, value addition and non-chemical supply chain

b) Industry

- Innovation Development with latest technology i.e. industry 4.0, robotic and IOT - Develop key enablers for ease of doing business - Promote Industrial Ecology - Develop marketing for niche groups such as elderly, creativity products and health care - Develop workforce competency for hi-tech and future industry - Logistic management using Natioanl Single Window

8

c) Services and Tourism

- Develop capability to adopt and adapt for change - Sustain and balance growth among stakeholders - Utilize latest technology (such as cloud) to leverage new service sector (digital services, education and content businesses) - Promote Cluster management - Expand eco-tourism

d) Eco-Friendly (Environment)

- Water conservation and utilization - Reduce pollution for better health and eco-system - Disaster prevention - Biodiversity conservation - Provide eco-friendly infrastructure for urban growth - Promote eco-friendly consumption

e) Public Sector

- Enhance public sector productivity and good governance for central and local government - Integrate and share ICT infrastructure

Vietnam Enhancing the productivity and quality of products and goods of Vietnamese enterprises until 2020, 2010-2020 (Primer Minister, May 21, 2010) Key Numerical Targets Relevant for Productivity Period 1: 2010 to 2015: - Build newly 4.000 national standards (TCVN), ensuring synchronous TCVN for major products and goods of the economy, 45% of TCVN of system of national standards harmonized with international standards, regional standards - Management by national technical standards for 100% of the group of products or goods likely to endanger the safety, sanitation, environmental pollution - Establish a organization network of assessing conformity with technical regulations, conformity of national standards for key products, goods - Build capacity and quality movement in 40 provinces and cities throughout the country - Build a team of specialists, consultant staff on productivity and quality; organization or individual operating professionally on productivity and quality of products and commodities at the ministries, branches, localities and enterprises produce key products and commodities - 40,000 enterprises are guided application of advanced scientific and technological innovation, application management systems, models and tools to improve productivity and quality - 40% of enterprises manufacturing key products and commodities construct and implement projects on improving productivity and quality - To contribute to raising the proportion of the productivity of general factors (TFP) in gross domestic product (GDP) growth to 30% in 2015 Period 2: From 2016 to 2020: - Develop new 2,000 Vietnam Standard (TCVN); and 60% TCVN under the national standards harmonized with international standards, regional standards - 100% laboratory of quality of key products, commodities reaching international level - 60,000 enterprises are guided to apply advanced scientific and technology and technological innovation, applying management systems, models and tools of improving productivity and quality - Build capacity and quality movement in all provinces and cities throughout the country - 100% of enterprises producing key products and goods develop and implement key projects of improving productivity and quality - To contribute to raise the proportion of the productivity to general factors (TFP) in gross domestic product growth (GDP) to at least 35% by 2020 - Build 1000 specialists, consultant staffs, trainers on productivity and quality a) Establish and apply the standards, - Establish and popularize the application of standards and technical regulations technical regulations - Establish a organization network of assessing conformity with technical regulations, conformity of national standards for key products, goods - Invest the testing laboratories of product and goods quality

143





> Vietnam (continued from previous page)

144

b) Productivity and Quality Promotion

- Propaganda, train and enhance knowledge on productivity and quality - Popularize the application of management systems, models, tools to improve productivity and quality - Promote the application of scientific and technological advances and technological renewal in enterprises - Evaluate the quality of products and goods - Measure the productivity of the economy, industry, enterprises

c) Industry

- Increase the productivity and quality of key products and commodities in the priority and spearhead industry sectors by applying approciate productivity and quality measures/tools - Renovate the technologies and apply the advanced technologies in manufacturing in order to build up the quality and content of science and technology, the proportion of value added and the domestic value in industrial products

d) Agriculture

- Increase the productivity and quality of key agricultural products and commodities by applying approciate productivity and quality measures/tools - Apply and transfer the scientific and technological advances in manufacturing, cultivation techniques and application of new plants and animal breeds for high productivity and quality - Apply and renovate teh technology in the exploitation, preliminary processing, preservation and deep processing; Enhance the the value of key agricultural, forestry and fishery products and export commodities

e) Information and Communications

- Increase the productivity and quality of key products and commodities in the field of information and communication equipment production by applying approciate productivity and quality measures/tools - Strengthen the testing capacity of the quality of information and communication equipment - Enhance the capability of research and development, apply the renovate the technology in order to improve the quality of switchboards and terminals' products compliance with the national standards, international standards and export market standards

f ) Construction

- Increase the productivity and quality of key products and commodities in the field of construction materials production by applying approciate productivity and quality measures/tools - Use the advanced technologies and select the suitable equipment to ensure the production of high-quality products compliance with the national standards, international standards and export market standards - Toward the mineral resources efficiency, reduce fuel costs and environmentally friendly

g) Health

- Increase the productivity and quality of key products and commodities in the fields of vaccines and medical bioproducts, pharmaceuticals, medical equipment and supplies by applying approciate productivity and quality measures/tools - Apply and develop the advanced technologies in the production of traditional medicines, vaccines, bio-products, essential medical equipments - Invest and increase the testing capability the quality of drugs and pharmaceutical materials compliance with international standards in service of production, import and export of products and goods

h) Transport

- Increase the productivity and quality of key products and goods in the manufacture of means and transport equipment by applying approciate productivity and quality measures/tools - Invest and strengthen the testing capacity of specialized products - Technological innovation - Apply the advanced synchronous technologies in the production of key products and goods with high localization ratios compliance with export standards or equivalent to the quality of the same products of foreign countries

l) Vietnamese SMEs

- Enhance the productivity and quality of key local products and goods by applying approciate productivity and quality measures/tools compliance with the characteristics of localities and enterprises - Guide enterprises on exploiting information on quality standards, technical regulations, technologies and information on industrial property for application in production and business practices - Replace the backward technology, apply the advanced technology, master the transferred technology from abroad


Population and Demographic Dividend

Box 11

According to the United Nations (2017), the world’s population is estimated to reach 7.4 billion in 2015, of which Asian countries account for 59.9%. The region is by far the most populous in the world. China and India account for 18.9% and 17.7% of the world’s population, respectively. It has been observed that falling fertility rates and rising living standards go hand in hand, although the direction of causality is less certain. The evolution of the demographic structure implies dynamics in a society that are not captured by the overall population size or growth. As people’s economic behavior, aspirations, and needs vary at different stages of life, changes in a country’s age structure can have a significant impact on its economic growth via supply-side and demand-side impacts. The world’s fertility rate is converging to the replacement level (the level at which a country’s population stabilizes). According to the UN, the number of children a woman is expected to have in her reproductive years has dropped by more than half, from about 5.0 to 2.5 in the last 60 years, compared to the replacement level of 2.2 children, one of them a girl. There is regional divergence in this trend. In the last 60 years, the total fertility rate dropped from about 6.8 children to 2.4 in Central America, and from about 5.6 children to 1.6 (below the replacement level), in East Asia. In comparison, some parts of Africa have seen only a modest drop in total fertility, which today remains at more than five children per woman. What is even more staggering is the pace of change. For example, it took Britain over 130 years (1800–1930) to halve its fertility rate, while it took Korea only 20 years to achieve it. This is echoed around the world. This widespread social revolution has been heralded by a complex mix of economic and social development. Economic growth, greater access for women to education, income-earning opportunities, and sexual and reproductive health services, all have been contributing factors to this trend. Coupled with changes in the mortality rate, such a trend can dramatically alter the age profile of a country’s population, bringing with it economic implications.

8

The growth rate of the world’s population has slowed from its peak of around 2.0% in the 1970s to today’s 1.2% per year. With falling fertility rates, the UN projects the world’s population growth rate will decelerate to 0.53% per year by 2050 and further to 0.09% by 2100. Even so, the world population will still increase by one-third from today’s 7.4 billion to 9.8 billion in 2050 and a further 14% to 11.2 billion by 2100. These estimates are based on the medium-fertility variant, but with only a small variation in fertility, particularly in the more populous countries, the total could be higher (10.9 billion by 2050 and 16.5 billion in 2100) or lower (8.8 billion in 2050 and 7.3 billion in 2100). Figure B11.1 depicts this shift in the distribution of the world population with the share from the more developed regions gradually declining from 17.0% in 2015 to 13.3% in 2050 and 11.5% in 2100, compared with 32.1% in 1950. Conversely, the share of the least developed countries is depicted as rising from today’s 13.0% to a projected 19.6% in 2050 and 28.6% in 2100, up from 7.7% in 1950.

Billion

Least developed countries Less developed regions, excluding least developed countries More developed regions

10

4

6.6

6.8

6.7

4

4.3 0.2

0

6 5.7

0.3

Oceania Northern America Latin America and the Caribbean Europe Africa Other Asia 0.4 ASEAN 0.7 China 0.7 India

8

0.7

6

12 10

1.9

1.2

8

2

2.6

3.2

2.7

2

1.5 0.8

1.0

1.2

1.3

1950

1975

2000

2025

1.3

1.3

1.3

2050

2075

2100

0

0.5 0.3 0.6 0.4

1950

0.3 0.7 0.4 0.3 0.6 0.9

0.4 0.8

0.5 0.8 0.7

0.5 0.7 0.7

0.7 3.4 2.3

4.3

0.3 0.5 0.7

1.4

0.8 0.4 1.0

1.3

1.5

1.4

1.4

1.2

1.0

1.6

1.7

1.5

2050

2075

2100

1.3

0.5

0.6

1.0

1.4

1975

2000

2025

0.6

0.7 1.6

0.7 1.5

Figure B11.1 Distribution of the World’s Population in Different Regions, 1950–2100 Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision. continued on next page >

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Billion

12



1970

2050

1600 1400 1200 1000 800 600 400

scale by 50 millions

350 300 250 200 150 100 30 25 20 15 10 5 0

scale by 5 millions


2015

scale by 200 millions

Figure B11.3 shows the demographic make-up of countries in 2015 (the population proportions of the under-15 and over65 age groups, which together make up the dependent population). Ranking the countries by the share of old-age population filters the rich economies to the top end. These economies also have a relatively low share of the young-age group compared to less developed countries. This suggests that demographic transition tends to run parallel with economic progress, although the direction of causation is not certain. As countries move from high to low mortality and fertility rates, the demographic transition produces a “boom” generation that is larger than those immediately before and after it. As this boom generation gradually works through a nation’s age structure, it produces a demographic dividend of economic growth as people reach their prime.

Million

1800

Brunei Bhutan Fiji Bahrain Qatar Mongolia Kuwait Oman Singapore Lao PDR Hong Kong UAE Cambodia Sri Lanka Australia ROC Nepal Saudi Arabia Malaysia Myanmar Korea Thailand Turkey Iran Vietnam Philippines Japan Bangladesh Pakistan Indonesia US EU28 India China

According to the projection, Asia’s share will decline from its 59.9% today to 53.8% in 2050 and 42.7% in 2100, while Africa’s share will rise from today’s 16.2% to 25.9% and 39.9%, respectively. Figure B11.2 shows the current population size of individual Asian countries compared with the 1970 level and its 2050 projection. As can be seen from the chart, China’s population is expected to more or less stabilize around the current level. China has socially engineered the change with its one-child policy, which has made its current population 300–400 million lower than it would have been otherwise. In less than two decades, India is projected to overtake China as the most populous country in the world.

Figure B11.2 Asian Countries’ Population Size and Projection, 1970, 2015, and 2050 Source: World Bank, World Development Indicators 2016.

18.8 19.1

Japan Hong Kong Australia US Korea ROC Singapore Thailand China Turkey Sri Lanka Brunei Vietnam Malaysia Iran Fiji Nepal India Philippines Myanmar Indonesia Bangladesh Bhutan Cambodia Pakistan Mongolia Lao PDR Saudi Arabia Oman Bahrain Kuwait UAE Qatar

12.5 11.3

13.8 13.6 15.5 17.7 16.5

24.0 25.2 23.6 23.1 25.3 23.6 28.7 31.3 28.8 30.5 25.9 27.7 29.4 29.7 31.0 35.0 28.1 35.9 28.6 20.5 22.3 22.3

40 % Age 0–14

30

15.0 15.7

20

10

0

8.2 7.9 7.2 6.7 5.9 5.9 5.8 5.8 5.6 5.4 5.2 5.2 5.0 4.8 4.6 4.5 4.0 3.8 2.9 2.6 2.5 2.0 1.0 0.9

0

5

26.3

15.3 15.0 14.9 12.8 12.5 11.7 10.5 10.5

10

15

20

25 %

Age over 65

Figure B11.3 Proportion of the Dependent Population, 2015 Sources: Population census and official national accounts in each country. continued on next page >

146



Using demographic data since 1950 and UN projections up to 2100, Figures B11.4 and B11.5 track changes in the ratio of the working population (aged 15-64) to dependent population (aged under 14 and over 65) by country and by country group, respectively. The higher the ratio, the more favorable its demography for economic growth. Japan could have capitalized on the demographic dividend in the 1960s, when its GDP growth was over 10% on average per year for ten years. Similarly, China, Hong Kong, Korea, Singapore, and Thailand are poised for the prospect of such demographic dividend in the 2000s and 2010s, whereas, based on projections, some ASEAN counties as Myanmar and Indonesia will have to wait for such opportunity until the 2020s and 2030s, and South Asian countries (except Sri Lanka) until the late 2030s and 2040s.

Dependent population (age under 14 and over 65)=1.0 (East Asia)

3.0 2.5 2.0

China Hong Kong Japan Korea Mongolia

1.5 1.0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Dependent population (age under 14 and over 65)=1.0 (South Asia)

3.0 2.5 2.0

Bangladesh Bhutan India Nepal Pakistan Sri Lanka

1.5

8

1.0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Dependent population (age under 14 and over 65)=1.0 (ASEAN6)

3.0 2.5 2.0

Brunei Indonesia Malaysia Philippines Singapore Thailand

1.5 1.0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Dependent population (age under 14 and over 65)=1.0 (CLMV)

3.0 2.5

Cambodia Lao PDR Myanmar Vietnam

2.0 1.5 1.0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure B11.4 Demographic Dividend by Country, 1950– 2100 Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision.

Dependent population (age under 14 and over 65)=1.0

3.0 2.5

East Asia South Asia ASEAN6 CLMV

2.0 1.5 1.0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure B11.5 Demographic Dividend by Country Group, 1950–2100 Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision.

147


The reaping of this dividend, however, is far from automatic. A favorable demography can work wonders to produce a virtuous cycle of wealth creation only if it is combined with appropriate health, labor, financial, human capital, and growth-enhancing economic policies. The presence of these complementary factors cannot be taken for granted, but needs to be cultivated in order to earn the demographic dividend. As the analysis of the Databook shows, the contribution of labor to economic growth has been smaller than those of capital and TFP for most countries (Figure 52 in Section 5.3, p. 70). This means that countries should not be afraid of aging too much as long as fairly high growth rates of capital and TFP are maintained. Nevertheless, understanding the demographic shift and its implications is highly relevant for economic projections, providing valuable foresight for economic policy making.

Appendix A.1

GDP Harmonization

The Databook incorporates some significant revisions to the national accounts. New developments for upgrading their national accounts based on the 2008 SNA have resulted in Sri Lanka as of March 2016 and Japan and Turkey as of December 2016. Based on our Metadata Survey 2017 for the APO member economies (see Box 2, p. 32), 11 economies are already 2008 SNA-compliant in Asia and others (Cambodia, Iran, the Lao PDR, Nepal, Thailand, and Vietnam) are 1993 SNA-compliant, although it should be noted that the extent of compliance in terms of coverage may vary. The different statuses of SNA adaptions among economies are responsible for the huge variations of data definitions and coverage in national accounts, calling for data harmonization to better perform comparative productivity analyses.

App.

This edition largely follows the concepts and definitions of the 2008 SNA and tries to reconcile the national accounts variations, in particular on the difference in the treatment of research and development (R&D), military weapon systems, software investment, and financial intermediation services indirectly measured (FISIM).126 In order to create long-time series data for the Databook, it is necessary to use the past estimates based on the 1968/1993 SNA, with exceptions in the ROC, Korea, and Singapore, who already published the backward estimates based on the 2008 SNA from the 1950s or 1960. In addition, some additional adjustments are necessary to harmonize the long-term estimates of GDP. Procedures for these adjustments are explained below.

1) FISIM FISIM is an indirect measure of the value of financial intermediation services provided, but for which financial institutions do not charge explicitly (United Nations, 1993: para. 6.124). It represents a significant part of the income of the finance sector. The 1993 SNA recommends that FISIM should be allocated to users (to individual industries and final demands). This is in contrast to the 1968 SNA, where the imputed banking services were allocated exclusively to the business sector. The common practice was to create a notional industry that buys the entire service as an intermediate expense and generates an equivalent negative value added. As such, the imputed banking services have no impact on GDP. Therefore, the 1993/2008 SNA recommendation, if fully implemented, will impact industry GDP and the overall GDP for the total economy (by the part of FISIM allocated to final demands).

126: The introductions of the 2008 SNA are usually conducted with the benchmark revisions. Thus in some countries there are large revisions in data due to the uses of the newly available survey (e.g. a new survey on services) or of the new benchmark data (e.g. a new development of the supply and use table), not largely due to the revisions from the 1993 SNA. The information required to reconcile the different benchmark-year series is collected for the APO member countries through our questionnaire to the national experts in our project.

149


Among the 20 APO member economies, four countries – Cambodia, the Lao PDR, and Nepal – do not allocate FISIM to final demands in their official national accounts, as a result of them not following the 1993/2008 SNA recommendation. Thus, the GDP values in these countries are smaller than others by definition. In addition, in the countries whose national accounts follow the 1993/2008 SNA’s recommendation on FISIM, the available data sometimes does not cover the entire periods of our observations. To harmonize the GDP concept among countries and over periods, final demands of FISIM are estimated for those countries in the APO Productivity Database, using available estimates of value added in Imputed Bank Service Charge (IBSC) or financial intermediation (in instances where IBSC data is not available). The ratios of value added of IBSC or financial intermediation on FISIM allocated to final demand are assumed to be identical with the average ratios observed in the countries in which data is available. Figure 99 describes the countries, years, and methods to adjust FISIM in the official national accounts. As described, in instances where both value added data are not available,

Appendix

the trend of the FISIM share on GDP is applied to extrapolate past estimates (although the impacts on GDP are minor). Figure 100 plots per capita GDP levels in 2015 and the FISIM share in GDP in 2000–2015 (including both of the original estimates in the official national accounts and our estimates). In countries where GDPs are adjusted, the proportions by which author adjustments for FISIM increases GDP stand at 0.6–1.2% for Nepal and the Lao PDR and less than 0.4% GDP in others.

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Bangladesh Cambodia Fiji India Indonesia Iran Japan Lao PDR Malaysia Mongolia Nepal Pakistan Philippines Sri Lanka Thailand Vietnam Bhutan Brunei Myanmar Bahrain Kuwait Oman Qatar Saudi Arabia UAE Turkey

1950 1951

1980 1993 1977

1960 1959 1955 1981 1985 1970

1955 1960

1974

1964 1960 1946

1967 1959 1951

1972 1970

1955

1986 1970 1970 1974

1980

1958

1990 1970 1975 1982

1962 1970 1970 1975 1970 1970 1975 1960

Adjustment using value added of imputed bank service charge Adjustment using value added of financial intermediation Adjustment using the average trend of FISIM share in GDP

2) Software The 2008 SNA recommends the capitalization of intellectual property products (IPP), which changes not only the size of GDP but also the size of capital input. One of the IPP capitalized in the Databook is computer software, which includes prepackaged software, custom software, and own-account software. Among APO member economies, 13 economies have capitalized all three types of software. Another three countries exclude own-account software in their capitalization and in one country only custom software is capitalized. For the APO Productivity Database, tentative adjustments have been made to harmonize data to include all software.

1970 1970 1970 1970

Figure 99 Adjustment of FISIM Source: APO Productivity Database 2017.

% FISIM share in GDP (average in the 2000−2015)

3.5

Hong Kong 3.0

2.5 Australia 2.0

1.5

India Philippines China Fiji

Thailand

Bhutan

Indonesia

Pakistan Nepal Vietnam 1.0 Iran Bangladesh Mongolia 0.5

Singapore

Korea Malaysia Turkey

Japan US ROC

Brunei

Lao PDR Cambodia


Sri Lanka

Thousands of US dollars (as of 2015) Myanmar Among the countries studied, the 0.0 0 10 20 30 40 50 60 70 80 90 data for software investment is Per capita GDP in 2015 (using 2011 PPP, reference year 2015) available for Bangladesh, the ROC, Offical national accounts in each country, including author adjustment Our estimates using value added in financial intermediation Japan, Korea, Malaysia, Mongolia, Our estimates using value added in imputed bank service charge Pakistan, the Philippines, Singapore, Thailand, and China. To harmonize Figure 100 FISIM Share in GDP, 2000–2015 data, a country’s GDP is adjusted to Sources: Official national accounts in each country and author estimates. include software investment (through its software industry) by using the ratio between software investment and GDP (software ratio) and the tangible GFCF to GDP ratio (GFCF ratio). Data from the OECD Productivity Database and the APO Productivity Database suggest an inverse relationship between these

150

A.1 GDP Harmonization

two ratios (Figure 101). Countries with a low GFCF ratio tend to be those with high per capita GDP, and the observed data suggest that IT tends to play a more important role in these countries than in lessdeveloped countries.

%

2.5

Software Investment/GDP

Sweden 2.0

1.5

US Japan Denmark Finland Korea Hong Kong France Switzerland New Zealand UK ROC Netherlands Canada

The Databook applies the inverse 1.0 relationship between these two raSingapore Germany Austria Spain tios observed from the OECD counItaly tries and national accounts in Asian Lao PDR Vietnam 0.5 non-OECD countries to estimate the Cambodia Fiji Indonesia China software ratio in 2006 for those APO Philippines Nepal Sri Lanka Ireland India Portugal Thailand member economies that do not Bangladesh Pakistan Malaysia Iran 0.0 Mongolia capitalize software investment. The 13 18 23 28 33 38 43 % Tangible GFCF/GDP estimated ratios for individual counCountries in which software is included tries in 2006 gradually taper off as Countries in which software is excluded Countries in which pre-package and own account software are excluded one moves back in time. However, Countries in which own account is excluded there is an exception. Countries at the very early stage of economic Figure 101 Software Investment Ratio and GFCF Ratio to growth are found to have a GFCF raGDP, 2005 tio as low as countries with high per Sources: OECD Productivity Database, including author adjustments. capita GDP, but for a different reason. The low GFCF ratio is explained by the fact that these countries have not experienced economic development yet, and in turn this does not play an important role for software investment. In this report, Cambodia, the Lao PDR, and Nepal are regarded as countries at the very early stage of economic development, and are assigned Vietnam’s software ratio accordingly, which is the lowest of all APO member economies.

App.

Another challenge arises from partial software capitalization. There are three types of software: prepackaged software, custom software, and own-account software. Countries may have capitalized one or two types of software, but software investment data is often not available separately. The Databook attempts to adjust for the varied level of capitalization across countries by adding the type of software not capitalized to countries’ GDP.

Valuables are defined as “goods of considerable value that are not used primarily for purposes of production or consumption but are held as stores of value over time” (United Nations, 1993: para. 10.7). They are held under the expectation that their prices will not deteriorate and will rise in the long run. Valuables consist of precious stones and metals such as diamonds; artwork such as paintings and sculptures; and other valuables such as jewelry made from stones and metals. In a small number of countries, such as India, Iran, Mongolia, Sri Lanka, Vietnam, and Bhutan, net acquisitions of valuables are recorded as a part of gross capital formation. For example, the SNA in India has included it since 1999, accounting for 1.4% of GDP for India on average during 1999–2015. The current decision is to harmonize the data by excluding net acquisition of valuables from GDP in the Databook.

151


3) Valuables

Appendix

4) Consumption of Fixed Capital of Assets Owned by Government

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Bangladesh Cambodia Fiji India Indonesia Iran Japan Lao PDR Malaysia Mongolia Nepal Pakistan Philippines Sri Lanka Thailand Vietnam Bhutan Brunei China Myanmar Bahrain Kuwait Oman Qatar Saudi Arabia UAE Turkey

1970 1970 1970 1950 1951

1993

2002 2010

1958 2000 2001

At the end of 2011, Thailand official1959 2009 1955 ly switched to the 1993 SNA, and its 1981 2002 1955 1996 national accounts became compat1960 1970 1997 1964 1974 2009 ible with the 1993 framework for 1960 1990 the first time. In this series, govern1946 1950 1959 1996 ment consumption includes the 1951 1990 1955 2002 2011 consumption of fixed capital (CFC) 1970 1970 1989 2002 2005 owned by the government since 1952 1952 1958 1997 2003 1990. In order to construct the long 1970 time-series data in the Databook se1962 1997 1970 2011 ries, the past data based on the 1970 1970 2003 1968 SNA has been adjusted to be 1970 2011 1960 1970 consistent with the new series. In Adjustment using R&D expenditure the Databook, government capital Adjustment using the average trend of R&D share in GFCF stock and its CFC for the period Adjustment using the average trend of R&D share in GDP 1970–1989 are estimated and the past government consumption and Figure 102 Adjustment of R&D GDP are adjusted accordingly. A Source: APO Productivity Database 2017. similar adjustment on the CFC of the assets owned by government was conducted for Bangladesh (for the period 1970–1995), Malaysia (1970–1999), and Mongolia (1970–2004). 1970

5) R&D The Databook capitalizes the R&D by following the 2008 SNA recommendations. In the countries that still do not follow the 2008 SNA, the R&D expenditures are not allocated to GFCF (but to intermediate uses). As a result the GDP values in these countries are smaller than others by definition. To harmonize the GDP concept among countries and over periods, the R&D investment is estimated for those countries in the APO Productivity Database. As a preferable approach, the data on the R&D expenditure are collected based on the official surveys in each country, in order to estimate the R&D investment. Figure 102 describes the countries, years, and methods to estimate R&D investment and adds it to GFCF in the official national accounts. If the data on R&D expenditures are not available, as a crude estimate, the trend of R&D investment shares on GFCF or GDP are applied to extrapolate past estimates. Figure 103 plots the per capita GDP and the R&D investment share in GDP in 2015. The impacts on GDP by our adjustment of the additional R&D investment are less than 1.0% of GDP for all countries in 2015.


6) GDP at basic prices GDP can be valued using different price concepts: factor cost, basic prices, and market prices. If the price concept is not standardized across countries, it will interfere with the international comparisons. All the countries covered in this Databook officially report GDP at market prices (or at purchasers’ prices), but this is not true for GDP at factor cost and GDP at basic prices. International comparisons in Chapter 3 (on economic scale and growth) and Chapter 4 (on final demand) are based on GDP at market prices. However, by valuing output and input at the prices that producers actually pay and receive, GDP at basic prices is a more appropriate measure of countries’ output than GDP at market prices for international comparisons of TFP and industry performance, as it is a measure from the producers’ perspective. Hence, Chapter 5 on whole-economy productivity performance is based on GDP at basic prices, including our estimates.

152

A.1 GDP Harmonization

% R&D share in GDP, 2015 These concepts of GDP differ 4.5 in the treatment of indirect tax Korea 4.0 and subsidies (and import duJapan ties). The difference between 3.5 ROC GDP at basic prices and GDP at market prices is “taxes on 3.0 products” minus “subsidies on USA products.” “Taxes on products” 2.5 Singapore Australia are the indirect taxes payable 2.0 on goods and services mainly when they are produced, sold, China 1.5 and imported, and “subsidies Thailand on products” are subsidies 1.0 Turkey India Malaysia Bhutan payable on goods and services Philippines Hong Kong Myanmar Iran mainly when they are pro0.5 Nepal Mongolia Pakistan Thousands of US dollars (as of 2015) duced, sold, and imported. Cambodia Sri Lanka Brunei Indonesia 0.0 Since GDP at basic prices is Bangladesh 0 10 Fiji 20 30 40 50 60 70 80 90 Per capita GDP in 2015 (using 2011 PPP, reference year 2015) Lao PDR Vietnam available for some economies, Offical estimates of R&D investment such as Hong Kong, India, KoOur estimates based on official data of R&D expenditure, including author adjustments rea, Mongolia, Nepal, SingaOur estimates using the average trend of R&D share in GFCF Our estimates using the average trend of R&D share in GDP pore, and Sri Lanka, a GDP at basic prices calculation, needs Figure 103 R&D Share in GDP, 2015 to be constructed for all other Sources: Official national accounts, including author adjustments; Surveys on R&D in each countries. In order to obtain country; World Bank (2016). GDP at basic prices, “taxes on products” and “duties on imports” are subtracted from GDP at market prices, which are available for all the countries studied, and “subsidies on products” is added. The main data sources for estimating “taxes on products” and “subsidies on products” are tax data in national accounts, the IMF’s Government Finance Statistics, and the input-output tables in each country.

App.

153


Readers should bear these caveats in mind when interpreting the results in Chapter 6, since the definition of GDP by industry differs among countries due to data availability. GDP is valued at: factor cost for Fiji, and Pakistan; at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia, Nepal, Singapore, and Vietnam; at producers’ prices for Iran, the ROC, and the Philippines; and at market prices for Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, and Thailand. In this sense, APO industry data should be treated as a work in progress as it is difficult to advise on data uncertainty. These issues will be developed and examined in the future.

Appendix

A.2

Capital Stock


Table 21 Asset Classification and At present, about half of APO member economies publish Parameters in Hyperbolic Function estimates of capital stocks in their systems of national accounts. Even where estimates are available, users must be T β 1. Computer hardware 7 0.50 mindful of differences in methodologies and assumptions 2. Telecommunications equipment 15 0.50 used to estimate capital stock and its consumption, as well 3. Transportation equipment 15 0.50 as a large diversity in the treatment of quality adjustment 4. O ther machinery and equipment and 15 0.50 weapon systems in price statistics among countries. In the APO Productivity 5. Residential buildings 30 0.75 Database 2017, a harmonized framework is applied in esti6. Non-residential buildings 30 0.75 7. Other construction 40 0.75 mating capital stock and capital services, covering 21 8. Cultivated biological resources 10 0.50 Asian economies: Bangladesh, Cambodia, China, the ROC, 9. Research and development (R&D) 10 0.50 Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, the 10. Computer software 3 0.50 Lao PDR, Malaysia, Mongolia, Nepal, Pakistan, the Philip11. Other intellectual property products 7 0.50 pines, Singapore, Sri Lanka, Thailand, and Source: APO Productivity Database 2017. Vietnam, and the US as a reference country. Although the main data in the Databook basically covers the period from 1970, our stock Table 22 Input-Output Tables and Supply and Use Tables estimates have the different initial periods in the perpetual inventory method (PIM), to Input-Output Tables and Supply and Use Tables 1981/1982, 1986/1987, 1992/1993, 1993/1994, 2000, 2005/2006, ease the errors in our assumptions on the iniBangladesh 2010/2011 tial capital stock levels. The starting years for Benchmark (1981, 1986, 1991, 1996, 2001, 2004, 2006, 2011) ROC estimating capital stock based on the PIM is: Extended (1984, 1989, 1994, 1999, 2004) Annual (2006–2015) Fiji 1972, 1981, 2002, 2005, 2008 1901 for the US; 1951 for the ROC; 1952 for India 1993/1994, 1998/1999, 2003/2004, 2006/2007, 2007/2008 China; 1953 for Korea; 1955 for Japan; 1960 for Indonesia 1971, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010 Singapore; 1961 for Hong Kong; and 1970 for Iran 1962, 1973, 1974, 1986, 1988, 1991, 1999, 2001, 2004, 2011 Japan 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2011 other countries. The hyperbolic function is Benchmark (1960, 1963, 1966, 1970, 1975, 1980, 1985, 1990, used to measure capital stock and the same Korea 1995, 2000, 2005, 2010) Updated (1973, 1978, 1983, 1986-1988, 1993, 1998, 2003, 2006–2014) parameters have been applied for all counMalaysia 1978, 1983, 1987, 1991, 2000, 2005, 2010 tries in the Databook, as shown in Table 21. Mongolia

1963, 1966, 1970, 1977, 1983, 1987, 1997, 2000, 2005, 2010

Pakistan

1975/1976, 1984/1985, 1989/1990, 1999/2000

Quality changes in the aggregate measure of Philippines 1961, 1965, 1969, 1974, 1979, 1985, 1988, 1994, 2000, 2006 Singapore 1973, 1978, 1983, 1988, 2000, 2005, 2007, 2010, 2012, 2013 capital input can originate from two kinds of Sri Lanka 2006 sources, namely the composition change by Thailand 1975, 1980, 1985, 1990, 1995, 1998, 2000, 2005, 2007, 2010 type of asset, and the quality improvement in Vietnam 1989, 1996, 2000, 2007, 2012 China 1987, 1992, 1997, 2002, 2007, 2012 each type of asset. To take the composition Brunei 2005, 2010 change of assets into account, the current daTurkey 1973, 1979, 1985, 1990, 1996, 1998, 2002, 2012 tabase classifies 11 types of assets (as shown Note: This edition of the Databook newly reflects China’s table in in Table 21). For countries in which detailed 2012, Vietnam’s table in 2012, and the ROC’s table in 2015. investment data is not available from national accounts, the 11 types of investment data are estimated based on the benchmark and/or annual input–output tables (IOT) or supply-use table (SUT) and our own estimates on the commodity flow of domestic production and export/import of assets. The IOT and SUT used in our measurement are listed in Table 22. This edition of the Databook newly reflects China’s table in 2012, Vietnam’s table in 2012, and the ROC’s table in 2015. It is well known that prices of constant-quality IT capital have been falling rapidly. For cross-country comparisons, it has been noted that there is great diversity in the treatment of quality adjustment in

154

A.2 Capital Stock

Pakistan

Cambodia

India

Philippines

US

Vietnam

Malaysia

Sri Lanka

Bangladesh

Fiji

Singapore

Lao PDR

Hong Kong

ROC

Mongolia

Iran

Thailand

Indonesia

Nepal

China

Korea

Japan

GDP=1.0 price statistics among coun4.5 1980 2015 4.0 4.0 tries. Cross-country com3.6 3.5 3.5 3.3 parisons will be significantly 3.1 3.1 3.1 3.0 2.9 3.0 2.9 3.0 2.7 2.7 2.7 2.6 biased if some countries ad2.6 2.6 2.5 2.5 2.4 2.4 2.4 2.3 2.5 2.3 2.2 2.2 2.2 2.1 just their deflators for quali2.0 1.9 1.9 2.0 1.8 1.8 1.8 1.8 2.0 1.7 1.7 1.7 1.6 ty change while others do 1.3 1.5 1.2 not. Price harmonization is 1.0 0.7 sometimes used in an at0.5 tempt to control for meth0.0 odological differences in the compilation of price indexes, under the assumption that individual countries’ price Figure 104 Capital-Output Ratio, 1980 and 2015 ___Ratio of the beginning-of-period net capital stock to basic-price GDP at data fails to capture quality improvements. Assuming that current prices the relative price of IT to Source: APO Productivity Database 2017. Note: The estimate for Cambodia is not available for 1980. non-IT capital in the countries compared is set equal to the IT to non-IT prices X + (∆ ln PITref − relative in the reference country, the harmonized price is formulated as: ∆ ln P˜ITX = ∆ ln PnlT ref ∆ ln PnlT ), where the superscript X denotes the country included in the comparisons, PIT is the price of IT capital, and PnIT is the price of non-IT capital. The price of IT capital in country X, P˜ITX , is computed by ref X in the reference country and PnlT in X. Schreyer (2002) and Schreyer, the observed prices PITref and PnlT Bignon, and Dupont (2003) applied price harmonization to OECD capital services, with the US as a reference country, since the possible error due to using a harmonized price index would be smaller than the bias arising from comparing capital services based on national deflators.

App.

Figure 104 presents the estimated capital-output ratio (stock coefficient) that is defined by the ratio of the beginning-of-period net capital stock (all types of produced fixed assets owned by private and public institutions) to the basic-price GDP at current prices. Japan has the highest capital-output ratio among Asian countries, at 3.6. However, the ratio may not work well for cross-country comparisons since the price differential between that for GDP and fixed assets in each country is not calculated. Compared to the 1980 level in each country, all Asian countries except Mongolia, Pakistan, and the Philippines have an increasing trend of capital-output ratio, unlike the ratio in the US, which is stable. 127: See OECD (2017a) and the website of the OECD productivity statistics (http://www.oecd.org/std/productivity-stats/). The project appreciates Maria Belen Zinni (Statistics Directorate, OECD) for her supports.

155


In this Databook, the same price harmonization method is applied to adjust the quality improvement for IT hardware and communications equipment in countries where the appropriate quality-adjusted price data is not available, with Japan’s prices as a reference country. A similar procedure was applied in cases where the prices for some assets were not available, to estimate missing data based on the relative price of these assets to total GFCF. In measuring capital services, this Databook largely follows the framework of the OECD Productivity Database.127 The OECD assumes the truncated normal distribution as profiles for asset discarding (retirement) and the hyperbolic distribution as profiles for asset decaying. The age-efficiency profile is defined as a combined distribution of discard and decay of assets. The age-efficiency profile in each asset is based on the two parameters in the hyperbolic function: T (average service life) and β (–∞